Extreme weather event attribution predicts climate policy support across the world

Viktoria Cologna; Simona Meiler; Chahan M Kropf; Samuel Lüthi; Niels G Mede; David N Bresch; Oscar Lecuona; Sebastian Berger; John Besley; Cameron Brick; Marina Joubert; Edward W Maibach; Sabina Mihelj; Naomi Oreskes; Mike S Schäfer; Sander van der Linden; TISP Consortium

doi:10.1038/s41558-025-02372-4

. 2025 Jul 1;15(7):725–735. doi: 10.1038/s41558-025-02372-4

Extreme weather event attribution predicts climate policy support across the world

Viktoria Cologna ^1,^2,^3,^✉, Simona Meiler ^1,⁴, Chahan M Kropf ^1,⁴, Samuel Lüthi ⁵, Niels G Mede ⁶, David N Bresch ^1,⁴, Oscar Lecuona ⁷, Sebastian Berger ⁸, John Besley ⁹, Cameron Brick ^10,¹¹, Marina Joubert ¹², Edward W Maibach ¹³, Sabina Mihelj ¹⁴, Naomi Oreskes ³, Mike S Schäfer ⁶, Sander van der Linden ¹⁵; TISP Consortium

¹Department of Environmental Systems Science, ETH Zurich, Zurich, Switzerland

²Collegium Helveticum, Zurich, Switzerland

³Department of the History of Science, Harvard University, Cambridge, MA USA

⁴Federal Office of Meteorology and Climatology MeteoSwiss, Zurich-Airport, Switzerland

⁵Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland

⁶Department of Communication and Media Research, University of Zurich, Zurich, Switzerland

⁷Department of Psychobiology and Methodology, Faculty of Psychology, Universidad Complutense de Madrid, Madrid, Spain

⁸Institute of Sociology, University of Bern, Bern, Switzerland

⁹Department of Advertising and Public Relations, Michigan State University, East Lansing, MI USA

¹⁰Department of Psychology, University of Amsterdam, Amsterdam, the Netherlands

¹¹Department of Psychology, University of Inland Norway, Lillehammer, Elverum Norway

¹²Centre for Research on Evaluation, Science and Technology, Stellenbosch University, Stellenbosch, South Africa

¹³Centre for Climate Change Communication, George Mason University, Fairfax, VA USA

¹⁴Centre for Research in Communication and Culture, Department of Communication and Media, Loughborough University, Loughborough, UK

¹⁵Department of Psychology, University of Cambridge, Cambridge, UK

¹⁶Institute of Malaysian and International Studies, National University of Malaysia, Bangi, Malaysia

¹⁷School of Collective Intelligence, Mohammed VI Polytechnic University, Ben Guerir, Morocco

¹⁸Department of Science and Technology Studies, Faculty of Science, Universiti Malaya, Kuala Lumpur, Malaysia

¹⁹ELTE Institute of Psychology, Eotvos Lorand University, Budapest, Hungary

²⁰School of Psychology, University of Sheffield, Sheffield, UK

²¹Department of Economics, University of Bath, Bath, UK

²²School of Psychology, University of Sussex/University of Amsterdam, Amsterdam, the Netherlands

²³Department of Philosophy, Macquarie University, Sydney, Australia

²⁴Department of Biochemistry, Faculty of Science, University of Dschang, Dschang, Cameroon

²⁵Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA USA

²⁶Linde Center for Science, Society, and Policy, Division of Humanities and Social Science, California Institute of Technology, Pasadena, CA USA

²⁷Department of Physics, Egerton University, Egerton, Kenya

²⁸Department of Communication, George Mason University, Fairfax, VA USA

²⁹Department of Psychology, Universitas Islam Negeri Sunan Kalijaga, Yogyakarta, Indonesia

³⁰Department of Interdisciplinary Social Science, University of Utrecht, Utrecht, the Netherlands

³¹National Institute of Science and Technology on Social and Affective Neuroscience, São Paulo, Brazil

³²Museum of Natural Sciences ‘Sabiha Kasimati’, University of Tirana, Tirana, Albania

³³Department of Architecture, University of Cambridge, Cambridge, UK

³⁴Department of Biomedical Sciences, University of Botswana, Gaborone, Botswana

³⁵Institute of Political Science, University of St Gallen, St Gallen, Switzerland

³⁶Institute of Psychology, SWPS University, Warsaw, Poland

³⁷Faculty of Psychology, University of Warsaw, Warsaw, Poland

³⁸Cedars-Sinai Medical Center, Los Angeles, CA USA

³⁹Computer Science Department, Harvey Mudd College, Claremont, CA USA

⁴⁰Laboratoire de psychologie des Pays de la Loire, LPPL UR 4638, Nantes Université, Univ. Angers, Nantes, France

⁴¹Institute for Sociology of the Slovak Academy of Sciences, Bratislava, Slovakia

⁴²Department of Scientific and Innovation Culture, Spanish Foundation for Science and Technology, Madrid, Spain

⁴³Department of International and Political Sciences, University of Genoa, Genoa, Italy

⁴⁴Institute of Multimedia and Interactive Systems, University of Lübeck, Lübeck, Germany

⁴⁵Department of Health Law, Policy, and Management, Boston University School of Public Health, Boston, MA USA

⁴⁶Institute for Sociology, Slovak Academy of Sciences, Staré Mesto, Slovakia

⁴⁷Department of Psychology, Erzurum Technical University, Erzurum, Turkey

⁴⁸Network for Economic and Social Trends, Western University, London, Ontario Canada

⁴⁹Behavior in Crisis Lab, Institute of Psychology, Jagiellonian University, Cracow, Poland

⁵⁰Cambridge Zero, University of Cambridge, Cambridge, UK

⁵¹LP3C (Psychology Laboratory), Université Rennes 2, Rennes, France

⁵²TRANSOC, Complutense University of Madrid, Madrid, Spain

⁵³Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria

⁵⁴Institute of General Practice and Family Medicine, University of Bonn, University Hospital Bonn, Bonn, Germany

⁵⁵School of Psychology, University of Kent, Canterbury, UK

⁵⁶Harding Center for Risk Literacy, University of Potsdam, Potsdam, Germany

⁵⁷Center for Sociocultural Research, HSE University, Moscow, Russia

⁵⁸Department of Labor and Social Policy, University of Lodz, Lodz, Poland

⁵⁹School of Psychological Science and Public Policy Institute, University of Western Australia, Perth, Western Australia Australia

⁶⁰Department of Management, Aarhus University, Aarhus, Denmark

⁶¹School of Psychology, University of Birmingham, Birmingham, UK

⁶²Department of Economics, Harvard University, Cambridge, MA USA

⁶³Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN USA

⁶⁴School of Communication and Culture, Aarhus University, Aarhus, Denmark

⁶⁵a2i Programme of ICT Division and UNDP Bangladesh, Dhaka, Bangladesh

⁶⁶Department of Resource Economics and Environmental Sociology, University of Alberta, Edmonton, Alberta Canada

⁶⁷Department of Political Science and International Relations, Carleton College, Northfield, MN USA

⁶⁸Faculty of Management and Economics, Ruhr-University Bochum, Bochum, Germany

⁶⁹Department of Philosophy, Macquarie University, Sydney, New South Wales Australia

⁷⁰LMU Munich School of Management, LMU Munich, Munich, Germany

⁷¹Leibniz Institut für Wissensmedien, Tübingen, Germany

⁷²School of Social Work, Lucerne University of Applied Sciences and Arts, Lucerne, Switzerland

⁷³Department of Psychology, Saarland University, Saarbrücken, Germany

⁷⁴Department of Psychology, Universidad de Concepción, Concepción, Chile

⁷⁵Institute for Management and Organization, Leuphana University, Lueneburg, Germany

⁷⁶School of Psychology, University of New South Wales, Sydney, New South Wales Australia

⁷⁷Institute for Climate Risk and Response, University of New South Wales, Sydney, New South Wales Australia

⁷⁸Research Institute for Responsible Innovation, School of Management, University of St Gallen, St Gallen, Switzerland

⁷⁹Department of Business Administration, Instituto Técnológico Autónomo de México, Mexico City, Mexico

⁸⁰Department of Media and Communication, LMU Munich, Munich, Germany

⁸¹Department of Geography, University of Bergen, Bergen, Norway

⁸²Centre for Climate and Energy Transformation, University of Bergen, Bergen, Norway

⁸³Faculty of Data and Decision Sciences, Technion—Israel Institute of Technology, Haifa, Israel

⁸⁴Faculty of Management, University of Warsaw, Warsaw, Poland

⁸⁵Centro de Investigación y de Estudios Avanzados del Instituto Politícnico Nacional, Mexico City, Mexico

⁸⁶Department of Media and Communication, City University of Hong Kong, Kowloon, Hong Kong

⁸⁷Leibniz-Institut für Wissensmedien, Tübingen, Germany

⁸⁸Department of Psychology, Eberhard Karls Universität Tübingen, Tübingen, Germany

⁸⁹School of Education, Trinity College Dublin, Dublin, Ireland

⁹⁰Department of Political Science and International Relations, KIMEP University, Almaty, Kazakhstan

⁹¹Department of Government and Politics, Jahangirnagar University, Dhaka, Bangladesh

⁹²Department of Psychology, University of Minnesota, Minneapolis, MN USA

⁹³School of Geography, Planning, and Spatial Sciences, University of Tasmania, Tasmania, Australia

⁹⁴Institute of Political Science, University of Bamberg, Bamberg, Germany

⁹⁵Institute of Political Science and Sociology, University of Bonn, Bonn, Germany

⁹⁶Doctoral School of Social Sciences, Nicolaus Copernicus University, Toruń, Poland

⁹⁷Hixon Center for Climate and the Environment, Harvey Mudd College, Claremont, CA USA

⁹⁸Department of Public Health, University of Otago, Wellington, New Zealand

⁹⁹Department of Journalism and Mass Communication, Lviv Polytechnic National University, Lviv, Ukraine

¹⁰⁰Institute of Communication Studies and Journalism, Charles University, Prague, Czech Republic

¹⁰¹Center for Social and Cultural Psychology, Université Libre de Bruxelles, Brussels, Belgium

¹⁰²Department of Political Science and International Relations, School of Sciences and Humanities, Nazarbayev University, Astana, Kazakhstan

¹⁰³Department of Social Research, University of Turku, Turku, Finland

¹⁰⁴INVEST Research Flagship Center, University of Turku, Turku, Finland

¹⁰⁵Faculty of Life Sciences: Food, Nutrition and Health, University of Bayreuth, Kulmbach, Germany

¹⁰⁶Department of Clinical and Health Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria

¹⁰⁷Division of Economics, Department of Management and Engineering, Linköping University, Linköping, Sweden

¹⁰⁸Faculty of Polish and Classical Philology, University of Adam Mickiewicz, Poznań, Poland

¹⁰⁹Department of Psychology, Ural Federal University, Yekaterinburg, Russia

¹¹⁰School of Psychology, Victoria University of Wellington, Wellington, New Zealand

¹¹¹Department of Information Science and Media Studies, University of Bergen, Bergen, Norway

¹¹²Department of Communication Science and Political Science, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands

¹¹³Faculty of Behavioural and Social Sciences, University of Groningen, Groningen, the Netherlands

¹¹⁴Laboratoire Parisien de Psychologie Sociale, Université Paris Nanterre, Nanterre, France

¹¹⁵Laboratory for Research of Individual Differences, University of Belgrade, Belgrade, Serbia

¹¹⁶School of Medicine and Psychology, Australian National University, Canberra, Australian Capital Territory Australia

¹¹⁷Uehiro Centre for Practical Ethics, University of Oxford, Oxford, UK

¹¹⁸Institut Langage et Communication, University of Louvain, Louvain, Belgium

¹¹⁹Departamento de Psicología, Universidad Peruana Cayetano Heredia, La Molina, Peru

¹²⁰Department of Psychology, University of Victoria, Victoria, British Columbia Canada

¹²¹Harvard Kennedy School’s Shorenstein Center, Harvard University, Cambridge, MA USA

¹²²Network Science Institute, Northeastern University, Boston, MA USA

¹²³School of Psychology and Public Health, La Trobe University, Melbourne, Victoria Australia

¹²⁴Department of Psychology, Royal Holloway, University of London, Egham, UK

¹²⁵School of Psychological and Social Sciences, University of Waikato, Tauranga, New Zealand

¹²⁶Department of Political Science and International Relations, University of Delaware, Newark, DE USA

¹²⁷Office for Quality Assurance, Analyses and Reporting, Project EUTOPIA, University of Ljubljana, Ljubljana, Slovenia

¹²⁸Department of Management and Supply Chain Studies, Nkumba University, Entebbe, Uganda

¹²⁹Department of Biochemistry and Molecular Biology, University of Buea, Buea, Cameroon

¹³⁰Communication Arts Programme, Bowen University, Iwo, Nigeria

¹³¹Department of Psychology and Psychotherapy, Witten/Herdecke University, Witten, Germany

¹³²Department of Management, University of Adger, Kristiansand, Norway

¹³³Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden

¹³⁴Institute of Psychology, University of Silesia in Katowice, Katowice, Poland

¹³⁵Penn Center for Neuroaesthetics, University of Pennsylvania, Philadelphia, PA USA

¹³⁶Institute of Medical Psychology, University of Tübingen, Tübingen, Germany

¹³⁷Department of Psychological Science, Pomona College, Claremont, CA USA

¹³⁸School of Psychology, Aston University, Birmingham, UK

¹³⁹Department of Psychology, University of Crete, Rethymno, Greece

¹⁴⁰Science Studies Laboratory, University of Warsaw, Warsaw, Poland

¹⁴¹Department of Psychology, University of the Philippines Diliman, Quezon City, Philippines

¹⁴²Graduate Institute of Journalism, National Taiwan University, Taipei, Taiwan

¹⁴³Max Planck Institute for Human Development, Berlin, Germany

¹⁴⁴Social and Cognitive Neuroscience Laboratory, Mackenzie Presbyterian University, São Paulo, Brazil

¹⁴⁵Institut des Géosciences de l’Environnement, University Grenoble Alpes, CNRS, IRD, Grenoble-INP, Grenoble, France

¹⁴⁶Institute of Environmental Health, Faculty of Medicine, University of Lisbon, Lisbon, Portugal

¹⁴⁷NOVA Institute of Communication, NOVA University of Lisbon, Lisbon, Portugal

¹⁴⁸Department of Nutritional Sciences, University of Vienna, Vienna, Austria

¹⁴⁹Department of Cognitive Psychology, Universität Hamburg, Hamburg, Germany

¹⁵⁰Psychology Department, TED University, Ankara, Turkey

¹⁵¹Sociology Department, Vrije Universiteit Brussel, Brussels, Belgium

¹⁵²School of Sustainability, Arizona State University, Tempe, AZ USA

¹⁵³Faculty of Political Science and Economics, Waseda University, Tokyo, Japan

¹⁵⁴Department of Civil Law, Faculty of Law, University of Tirana, Milto Tutulani, Tirana, Albania

¹⁵⁵Centre for the Politics of Feelings, University of London, London, UK

¹⁵⁶Division of Public Policy, Hong Kong University of Science and Technology, Hong Kong, Hong Kong

¹⁵⁷School of Psychology, University of Sussex, Falmer, UK

¹⁵⁸Molecular Haematology and Immunogenetics Laboratory, Department of Medical Laboratory Science, Faculty of Health Sciences and Technology, College of Medicine, University of Nigeria Nsukka, Nsukka, Nigeria

¹⁵⁹Department of Environmental Social Sciences, Stanford Doerr School of Sustainability, Stanford, CA USA

¹⁶⁰Faculty of Technology and Bionics, Rhine-Waal University, Kleve, Germany

¹⁶¹Faculty of Philosophy and Social Science, Nicolaus Copernicus University, Toruń, Poland

¹⁶²School of Economics and Management, Tongji University, Shanghai, China

¹⁶³School of Environment, Tsinghua University, Beijing, China

^✉

Corresponding author.

PMCID: PMC12237696 PMID: 40641510

Abstract

Extreme weather events are becoming more frequent and intense due to climate change. Yet, little is known about the relationship between exposure to extreme events, subjective attribution of these events to climate change, and climate policy support, especially in the Global South. Combining large-scale natural and social science data from 68 countries (N = 71,922), we develop a measure of exposed population to extreme weather events and investigate whether exposure to extreme weather and subjective attribution of extreme weather to climate change predict climate policy support. We find that most people support climate policies and link extreme weather events to climate change. Subjective attribution of extreme weather was positively associated with policy support for five widely discussed climate policies. However, exposure to most types of extreme weather event did not predict policy support. Overall, these results suggest that subjective attribution could facilitate climate policy support.

Subject terms: Climate-change impacts, Climate-change policy, Attribution, Psychology

Literature produced inconsistent findings regarding the links between extreme weather events and climate policy support across regions, populations and events. This global study offers a holistic assessment of these relationships and highlights the role of subjective attribution.

Main

Climate change is increasing the frequency and intensity of extreme weather events (defined as an event that is rare at a particular place and time of year¹), which puts a substantial proportion of the global population at physical and economic risk¹. The cost of extreme weather events attributable to climate change is estimated at US$143 billion per year². The impacts of extreme weather events are disproportionately felt in countries in the Global South³. Even though the Global South is at greater risk, attribution studies and social science research on human responses to such events overwhelmingly focus on countries and populations in the Global North^4–6.

Mitigative action is needed to slow climate change and mitigate the impacts of extreme weather events. So far, global efforts have been insufficient, which calls for more stringent climate policies. Public support for climate policies is important because such support can drive governmental policy outputs⁷ and policymakers often respond to public demand for climate policies⁸.

The psychological distance of climate change (that is, the perception that climate change is spatially, temporally and socially distant) may help explain societal inaction on this issue⁹. If so, public awareness and understanding of climate change may increase as more people experience extreme weather events for themselves^10–15. However, previous studies on the relationship between experiencing extreme weather events and climate change action and beliefs have produced inconsistent findings. In particular, some studies have found that experiencing extreme weather events increases climate change belief¹⁶, concern^11,17–19, support for climate policies and green parties^17,20–23, and climate change adaptation²⁴, while other studies found no relationship^6,25–27. Studies using aggregate objective measures of exposure to and impacts of extreme weather events often find no effect of extreme weather experience on climate change attitudes^25,26,28. For example, one US study found that living in an area with higher fatalities from extreme weather events was associated with perceiving more climate risks²⁹, while another US study found that fatalities from extreme weather events were not associated with opinions about climate change³⁰. However, these studies used different definitions and measurements of extreme weather events, and these extreme weather events were compared with different psychological and behavioural outcomes²⁷. Further, most studies have focused on a single country³¹ or a single type of extreme weather event (for example, heatwaves), which limits the comparability of the impacts of different types of extreme weather event. This limitation is considerable, as a meta-analysis found notable differences in effect sizes depending on the type of extreme weather event³².

The inconsistency of previous studies might also be explained by another important factor: whether people attribute the extreme weather event to climate change^{6,11,31,33–35}. Recent studies support this hypothesis: people who attribute extreme weather events to climate change are more likely to perceive climate change as a risk and to report engaging in mitigation behaviour^36,37. For example, a study in the United Kingdom found that the subjective attribution of floods to climate change is a necessary condition for the experience of floods to translate into climate change threat perception³⁶. However, no cross-country evidence exists on the subjective attribution of extreme weather events to climate change.

Current study

We combined natural and social science approaches to examine how extreme weather events and their attribution to climate change relate to support for widely discussed climate change mitigation policies across 68 countries (N = 71,922). This study employed an interdisciplinary design by triangulating data on exposed populations computed using the probabilistic CLIMADA risk modelling platform^38,39 with global survey data on subjective attribution of extreme weather events and support for climate policies collected in the Trust in Science and Science-related Populism (TISP) study⁴⁰. We used a standardized metric to comparatively assess the relationship between the size of exposed populations to several extreme weather events—river floods, heatwaves, European winter storms, tropical cyclones, wildfires, heavy precipitation and droughts—and climate policy support. Specifically, we modelled how many people in a country were exposed to extreme weather events over the past few decades relative to the total population. We referred to this as the ‘exposed population’ (see Online Methods).

Our preregistered study addressed the following research questions: (1) Does exposure to extreme weather events on the population level relate to climate policy support? (2) Do subjective attribution and exposed population have an interactive effect on policy support? In addition, we addressed the following non-preregistered questions: (1) What is the level of public support for five climate policies across countries? (2) To what degree do people attribute extreme weather events to climate change across countries (subjective attribution) and is subjective attribution related to policy support?

We hypothesized that people who live in countries with higher exposure would show stronger support for mitigative climate policies, and that the relationship between exposed population and policy support would be stronger for individuals with higher subjective attribution. We also hypothesized that the relationship between exposed population and policy support is associated with people’s income and residence area (urban vs rural), which might relate to their adaptation potential to extreme events. Note that not all preregistered questions are addressed in this paper.

Support for climate policies

We assessed support for the following five climate policies with a 3-point scale (1 = not at all, 2 = moderately, 3 = very much): Increasing taxes on carbon-intense foods, raising taxes on fossil fuels, expanding infrastructure for public transportation, increasing the use of sustainable energy, and protecting forested and land areas. In line with previous research, increasing carbon taxes received the lowest support^41,42, with only 22% and 29% of people, respectively, indicating they very much support increased taxes on carbon-intensive foods and fossil fuels (Fig. 1a). Protecting forested and land areas, by contrast, was a popular policy option, with 82% supporting it very much and only 3% not supporting it at all. The second most-supported policy was increasing the use of sustainable energy, with 75% supporting it very much, and only 5% not supporting it at all. For further analyses, we combined responses to the five policy options into an index (α = 0.61; see factor analysis in Supplementary Table 12 and non-preregistered analyses with policy subscales in Supplementary Fig. 7).

Fig. 1 — a, Weighted response probabilities for single items measuring support for climate policies. b, Mean support for climate policies in 66 countries (climate policy support was not measured in Argentina and Malaysia). Participants were asked: “Please indicate your level of support for the following policies.” Response option ‘not applicable’ is not shown. No data were available for countries shaded in light grey.

A clear majority supported climate policies in all countries (global mean (M) = 2.37, s.d. = 0.43 on a scale from 1 = Not at all, 2 = Moderately and 3 = Very much). These findings are in line with a previous study showing that 89% of participants demand intensified political action on climate change⁴³. We calculated mean support by averaging participants’ support for five policies (see Online Methods and Fig. 1). This mean value is representative in terms of gender, age and education due to post-stratification weighting (see Online Methods). We found strong differences in support across countries and policies (Fig. 1b). Support for climate policies was particularly high in African and Asian countries, average in Australia, Costa Rica and the United Kingdom, and below the global average in several European countries, such as Czechia, Finland and Norway (Supplementary Figs. 1–6). Non-preregistered analyses comparing our aggregate measure with policy support subscales (that is, support for taxes, support for green transition) can be found in Supplementary Fig. 7. Our results for the aggregate measure and policy subscales were mostly consistent.

Participants who identified as men, were younger, more religious, had higher education, higher income, left-leaning politics and who lived in urban areas were more likely to support climate policies (Supplementary Tables 1–7 and Fig. 8), in line with previous studies^44,45.

Subjective attribution

Participants indicated subjective attribution by rating the degree to which they believed that climate change has increased the impact of six extreme weather events—droughts, heatwaves, wildfires, heavy rain, floods, heavy storms—in their country over the past decades (1 = Not at all, 5 = Very much). Responses to the six items were mean averaged (α = 0.92). Globally, subjective attribution of extreme weather events to climate change was well above the scale midpoint in all countries (M = 3.80, s.d. = 1.02). In line with a previous study³⁶, non-preregistered analyses showed that subjective attribution was positively related to identifying as a woman, being older, more religious, having higher education and higher income, living in an urban (vs rural) area and self-identifying as politically liberal and left-leaning (Supplementary Table 8).

There was little variation in subjective attribution across extreme event types. Subjective attribution appeared relatively lower for wildfires (M = 3.67, s.d. = 1.28) and higher for heatwaves (M = 3.94, s.d. = 1.16). However, subjective attribution varied across global regions (Fig. 2). Participants in South American countries most strongly agreed that the occurrence of extreme weather events has been affected by climate change over the past decades, especially in Brazil and Colombia (Supplementary Fig. 9). Subjective attribution was lowest in Northern European and African countries (Supplementary Fig. 9). Lower subjective attribution in African countries could be explained by the fact that climate change awareness and belief in human-caused climate change are still relatively low across African countries⁴⁶.

Fig. 2 — Data from 67 countries. Subjective attribution was not assessed in Albania. No data were available for countries shaded in light grey.

Exposed population and policy support

The size of the exposed population varied by the type of extreme event (Fig. 3). While almost all the sampled populations were exposed to heatwaves and heavy precipitation over the past decades at least once, fewer populations had been exposed to droughts, wildfires and floods. Our fully anonymous data did not allow geospatially matching participants to certain areas where extreme events occurred; we therefore do not know whether participants were personally exposed to those events and cannot test whether exposure at the individual level relates to policy support. However, we can reliably estimate whether exposure at the population level relates to policy support.

We investigated whether exposure at the country level and subjective attribution of extreme events at the individual level were associated with stronger climate policy support. Since we were interested in studying how the relationships vary between different types of extreme weather event and policy support, we ran seven blockwise multilevel regression models—one for each type of extreme weather event—predicting an index of climate policy support. Because participants were clustered within countries, our models included random intercepts across countries. Step 1 of the blockwise regression included socio-demographic variables and exposed population. In Step 2, we added subjective attribution for the specific event and three interaction terms: exposed population × subjective attribution, exposed population × income and exposed population × residence area.

Belief that climate change has impacted local extreme weather events predicted support for climate policy (Fig. 4). Random effects models show that the relationship between subjective attribution and policy support was significantly stronger in North America, Australia and in several European countries than the mean global effect, and significantly weaker in Peru and South Africa (Supplementary Figs. 10–16).

For five out of the seven extreme weather events, exposed population size did not predict policy support (Fig. 4 and Supplementary Tables 1–7). However, people in countries more exposed to wildfires were more supportive of climate policies (Supplementary Table 5). Conversely, people in countries more exposed to heavy precipitation were less supportive of climate policies (Supplementary Table 3). We conducted additional exploratory, non-preregistered robustness checks to investigate whether exposed population and land area, as well as exposed population and climate change belief at the country level had an interactive effect on policy support. Since climate change belief was not assessed in this study, we relied on country-level data from another study⁴⁷, available for 48 countries included in this study. The relationship between exposure to heavy precipitation/wildfires and policy support was no longer statistically significant when controlling for beliefs and land area, while the relationship between subjective attribution and policy support remained significant (Supplementary Fig. 17). Therefore, the relationship between exposure to wildfires/heavy precipitation and policy support should be interpreted with caution.

We tested whether exposed population size and subjective attribution interacted to predict policy support, as investigated in previous studies^33,36,37. We found that the relationship between exposed population and policy support was stronger for participants with higher attribution of heatwaves and tropical cyclones, whereas the relationship between exposed population and policy support was weaker for participants with higher attribution of heavy precipitation and European winter storms. However, we found the opposite interaction effect for river floods, droughts and wildfires: as subjective attribution increases, the relationship between exposed population and policy support weakens. In other words, for individuals with high subjective attribution, support for policies is already high and less dependent on exposure to these extreme events. In contrast, for individuals with low subjective attribution, support for policies increases with higher exposure to droughts, floods and wildfires (Fig. 5).

Fig. 5 — The lines represent varying levels of subjective attribution at −1s.d, the mean and +1 s.d., with shaded regions indicating 95% confidence intervals. The x axis shows the standardized exposed population size.

These findings are in tension with the results of previous studies, which reported a positive moderation effect for flooding³⁶, a negative moderation effect for hurricanes³³ and no moderation effect for wildfires³⁷.

Interaction effects with income and residence area

Our seven multilevel models each included interaction effects for exposed population × income and exposed population × residence area. We found significant interactions with small effect sizes for river floods and wildfires, but not for any other events. For river floods, we found a negative interaction effect with income and a positive interaction with urban areas (Supplementary Table 4). This indicates that the relationship between exposed population size and policy support was stronger for individuals with lower income as well as for individuals who live in urban areas. For wildfires, we found a positive statistical effect for income, meaning that the relationship between exposed population and policy support was stronger for richer individuals (Supplementary Fig. 18).

Discussion

This study provides global evidence that subjective attribution of extreme weather events to climate change is associated with greater policy support for climate mitigation. Overall, different extreme weather events appear to have different relationships with climate policy support. This pattern highlights the importance of comparative analyses that consider different types of event.

We additionally provide evidence that subjective attribution is high, and particularly so in Latin America. This might be explained by the fact that belief in human-caused climate change and self-reported personal experience of extreme weather events are high in Latin America⁴⁸, and that people in Latin American countries were among the most likely to report that climate change will harm them and future generations a great deal and that climate change should be a high priority for their government⁴⁹. The finding that the relationship between subjective attribution and policy support was weaker in some Latin American countries might therefore be due to a ceiling effect

In line with previous studies³⁶, we also found that subjective attribution interacts with exposure to European winter storms, heatwaves, heavy precipitation and tropical cyclones to predict climate policy support. Mere exposure to extreme weather events might therefore not suffice to increase policy support unless individuals link these events to climate change³⁰. While larger exposure to extreme events was not found to be related to policy support (except for wildfires), we cannot rule out that changes in the frequency of extreme weather events over time might be sufficient to shift support. Nevertheless, our data suggest that if individuals attribute extreme weather events to climate change, support for climate policies is higher regardless of whether the events are more frequent. The reverse causal relationship is also possible: people who are supportive of climate policies are more likely to attribute extreme weather to climate change. Longitudinal panel studies are needed to investigate the nature and direction of this relationship.

These findings might also help explain previous inconsistent results on the relationship between extreme weather event experience and mitigation behaviour. Few of these studies assessed whether participants linked these events to climate change, therefore missing a key controlling variable. Consequently, we strongly recommend that future studies assess subjective attribution. We found a negative relationship between exposed population to heavy precipitation and policy support in our preregistered model. Subjective attribution was relatively low for heavy precipitation. This corroborates previous findings that people often fail to link extreme rainfall with climate change¹⁰. In line with this argument, a media analysis that investigated themes in climate change coverage in 10 countries (2006–2018) found that media reporting on extreme weather events mostly focused on weather anomalies, as well as fires, hurricanes and storms⁵⁰. Countries more exposed to heavy precipitation might therefore be less willing to support climate policies because they are less likely to link those events to climate change. Our moderation analyses show that the negative effect of heavy precipitation exposure on policy support is strongest for people with low subjective attribution. This further highlights the need for more research on climate change communication on types of extreme weather event that are not typically associated with climate change, such as heavy precipitation, as these events might serve as ‘teachable moments’¹⁵. However, it should be noted that the relationship between exposure to heavy precipitation and policy support was no longer significant in our exploratory analyses that included the interactions of exposed population with land area and climate change belief. This finding should therefore be interpreted with caution.

Wildfires are the only type of extreme weather event that positively predicts climate policy support when controlling for subjective attribution, although this effect was no longer significant in models that included interaction effects for exposure with land area and climate change belief. Several previous studies similarly reported a positive relationship between wildfire exposure and climate policy support^23,37,51,52. This positive relationship could be explained by the fact that wildfires often result in extensive and visible damage⁵¹, and are linked to personal health concerns due to smoke exposure⁵³. Another study found that among Australian adults who directly experienced wildfires, 45% increased individual climate activism, providing further evidence of the effects of wildfires on behavioural intentions⁵⁴.

Contrary to our hypothesis, the relationship between exposed population and policy support was weaker for individuals with higher subjective attribution of droughts, floods and wildfires. One possible explanation is that these three types of extreme weather event allow for management strategies that can directly reduce the hazard itself, such as man-made flood protections, irrigation systems, prescribed burn-offs and land-use policies. Therefore, people may be more likely to support policies pertaining to law enforcement or economic regulations instead of climate change mitigation^55,56. In contrast, although heavy precipitation, storms and heatwaves are exacerbated by climate change and can be mitigated by addressing it, once they occur, we can only manage their impacts, not prevent their occurrence. Future research should investigate these interactions and explore the possibility that the size of the exposed population moderates the relationship between subjective attribution and policy support, rather than subjective attribution moderating the effect between the size of the exposed population and policy support.

Our measure of exposed population has strengths and limitations. While the standardized metric of exposed population allows the comparison of the impacts of different events across countries, it is a relative measure (that is, to a country’s total population) and does not reflect the severity of exposure or the potential for individuals to be repeatedly exposed to different events. Further, the measure does not consider the exposure to compound events⁵⁷, that is, when two or more events occur in an interacting combination. No conclusions can be drawn as to whether the participants in the study were directly exposed to these events. This measure therefore reflects the broader population-level exposure to these events, rather than individual-level exposure. The data cannot speak to whether exposure at the individual level relates to policy support. However, it can be reliably concluded that exposure at the population level did not relate to policy support. Some extreme weather events are less likely to be experienced directly (for example, floods or hurricanes), but they still receive widespread media coverage. The approach of analysing exposure at the population level therefore allows the study of effects that go beyond individual exposure to events. It should be noted that for some extreme weather events (for example, heatwaves and heavy precipitation), variance was very low, given that most people were affected by these events at some points over the past few decades (Supplementary Table 9).

Since the measure of exposed population included the past few decades, the estimates here are probably conservative for the effects of exposure. Researchers have found that temporal proximity of an event matters for climate change concern: the more recent an event, the larger the impact on climate change concern¹⁸. Since some of these events occur infrequently (for example, tropical cyclones), longer time frames such as in this study have the advantage that they allow the comparison of the effects of several different events in a global context⁵⁸.

With the use of a measure of exposure to extreme weather events at the population level, this article finds that subjective attribution predicts climate policy support, while exposure to five out of the seven extreme events considered in this study does not predict policy support. Overall, ensuring subjective attribution might be an important way to increase support for climate policies³⁷. Experimental research could focus on finding effective communication strategies to increase subjective attribution among the public to help develop causal models (for example, ref. ⁵⁹). Extreme weather events are increasingly linked to climate change in news and social media^50,60–63, but more research is needed to study communication of extreme weather events and their attribution in the Global South^62,64.

Methods

Dataset

This study relies on the dataset collected for the TISP Many Labs study⁴⁰. Detailed information on the data collection strategy can be found in ref. ⁶⁵. Participants were asked to carefully read a consent form (approved under IRB protocol number IRB22-1046), which included some general information about the study and the anonymity of the data. Only participants who consented to participating in the study were allowed to proceed with the study.

Sample and weighting

Data were collected in surveys that used quotas for age (five bins: 20% 18–29 years, 20% 30–39 years, 20% 40–49 years, 20% 50–59 years, 20% 60 years and older) and gender (two bins: 50% men, 50% women). To generate models with parameters that are representative for target populations in terms of gender, age and education, and have more precise standard errors, we used post-stratification weights. Specifically, we computed post-stratification weights at country level, sample size weights for each country, post-stratification weights for the complete sample, and rescaled post-stratification weights for multilevel analyses.

Main measures included in the questionnaire

Climate policy support

Participants were asked: “Many countries have introduced policies to reduce carbon emissions and mitigate climate change. This can include the implementation of laws aiming to reduce greenhouse gases, for example. Please indicate your level of support for the following policies: 1) Raising carbon taxes on gas and fossil fuels or coal, 2) Expanding infrastructure for public transportation, 3) Increasing the use of sustainable energy such as wind and solar energy, 4) Protecting forested and land areas, 5) Increasing taxes on carbon intense foods (for example, beef and dairy products).” Response options ranged from 1 = Not at all, 2 = Moderately, 3 = Very much, and 4 = Not applicable. Response option 4 was coded as missing for the analyses.

Subjective attribution

Participants were asked: “The next questions are about climate change and weather events. When you answer them, please think about your country. To what extent do you think that climate change has increased the impact of the following weather events over the last decades? 1) Floods, 2) Heatwaves, 3) Heavy storms, 4) Wildfires, 5) Heavy rain, 6) Droughts.” Response options ranged from 1 = Not at all, to 5 = Very much.

See ref. ⁶⁵ for a detailed overview of the other measures.

Analyses

We submitted a detailed preregistration including research questions, hypotheses and an analysis plan to OSF (10.17605/OSF.IO/G23A7) before data collection on 15 November 2022.

To estimate the relationships between subjective attribution, exposed population and three interaction terms (exposed population × subjective attribution; exposed population × income log (US$); exposed population × residence area (urban vs rural)), we used blockwise multilevel regression models with random intercepts across countries. In addition, we computed models with random effects to estimate how the effects of subjective attribution on climate policy support varied across countries. We scaled all independent variables by country means and country s.d.s, except for the country-level variable ‘exposed population’, which we scaled with grand means and grand s.d.s.

We estimated the reliability of our two scales: subjective attribution and climate policy support. Scale reliability of subjective attribution in the global sample was very high, with Cronbach’s alpha = 0.92 and omega = 0.92. An overview of the reliability of subjective attribution across 67 countries (ranging from omega = 0.74 to omega = 0.95) can be found in Supplementary Table 10. Scale reliability of climate policy support in the global sample was acceptable, with Cronbach’s alpha = 0.61 and omega = 0.62. An overview of the reliability of climate policy support across 66 countries (ranging from omega = 0.40 to omega = 0.75) can be found in Supplementary Table 11. To further assess the robustness of our policy support scale, we ran a polychoric parallel analysis with principal axis factoring to inspect how many factors should be retained for an exploratory factor analysis (EFA). The parallel analysis determined that two factors should be kept for an EFA. We therefore ran an EFA with unweighted least squares factoring and promax oblique rotation to inspect two factor loadings (Supplementary Table 12). Our items clearly loaded on two factors, with items relating to the expansion of public transport, protected areas and increasing renewable energy loading on Factor 1 (labelled as ‘Green transition’) and the two items related to increasing taxes on meat and dairy and fossil fuels loading on Factor 2 (labelled as ‘Taxes’). The Taxes subscale had good internal reliability (omega = 0.73). The Green transition subscale had moderate, but still acceptable reliability (omega = 0.61), comparable with the reliability of the aggregate scale (omega = 0.62).

We further conducted three non-preregistered robustness checks. Specifically, we examined whether our results are robust to the inclusion of an interaction between land area of countries (in square kilometres) and exposed population, an interaction between country-level climate change belief and exposed population, and across the two climate policy support subscales (Taxes and Green Transition). Data on climate change belief were retrieved from the Climate Many Labs study as processed by Our World in Data⁶⁶, while data on land area were retrieved from multiple sources compiled by World Bank (2024) and processed by Our World in Data⁶⁷. Data on land area for Taiwan was retrieved from ref. ⁶⁸. The term ‘country’ in this Article refers to both sovereign states and territories not recognized as such.

Impact model CLIMADA

In this study, we used the open-source, probabilistic CLIMADA (CLIMate ADAptation) risk modelling platform^38,39 for the spatially explicit computation of exposed population from different hazards on a grid at 150 arc-seconds (~4.5 km at the equator) resolution. CLIMADA was designed to simulate the interaction of climate and weather-related hazards, the exposure of assets or populations to this hazard, and the specific vulnerability of exposed infrastructure and people in a globally consistent fashion. The platform has been developed and maintained as a community project, and the Python 3 source code is openly available under the terms of the GNU General Public License (v.3)³⁹.

Exposure

We used the Gridded Population of the World (GPW) dataset v.4.11, published in 2020 (CIESIN, 2018)⁶⁹, to map population exposure across the 68 countries. The GPW dataset was chosen for its high spatial resolution and its comprehensive and consistent coverage, providing population count estimates at a granularity of 30 arc-seconds (~1 km at the equator), which we aggregated to match the 150-arc-second resolution used in our risk model.

Hazards

Seven types of extreme weather event were analysed in this study: droughts, river floods, heatwaves, heavy precipitation, tropical cyclones, wildfires and European winter storms, which form the input hazard layer in our risk model. We computed the exposed population to these events. Detailed information on the definition of each event, data sources, the years covered and other relevant details for each type of extreme weather event are provided in Supplementary Table 13.

Each hazard in this study was defined on the basis of its unique characteristics and the potential impact it has on the exposed population, with the chosen underlying datasets ensuring consistent coverage across all countries involved. Some of these hazards were evaluated in an event-based perspective (for example, tropical cyclones, wildfires), while others were assessed as annually aggregated measures (for example, river floods, heatwaves). Hazards were inferred either from historical records (tropical cyclones, European winter storms, wildfires), climate reanalyses of a reference period (heatwaves, heavy precipitation) or historical climate modelling (droughts, river floods). In instances where multiple (climate) models contribute to the hazard modelling, we computed the multimodel median impact on the exposed population.

For drought, we utilized a ‘long-term’ definition based on soil moisture⁷⁰, a methodology that primarily captures agricultural impacts, potentially leading to indirect effects on populations. Furthermore, the dataset provides annual maxima, without representing single drought events, which potentially limits the depth of our risk analysis for certain areas.

In the case of river floods, the datasets used in this study represent large rivers and fluvial floods, while coastal or pluvial floods are not included^70,71. We note that ‘heavy precipitation’ as a different hazard may serve as a proxy for pluvial or flash floods. Besides, there was a potential overestimation of affected areas due to the methodology of considering full grid cells as affected.

For heatwaves and extreme precipitation events, we characterized the hazards on the basis of deviations from the 20-year reference period 1980–1999. We utilized ERA-5 reanalysis data to display observed trends as changes between the reference period and the more recent 20-year period 2000–2019⁷². Finally, changes were displayed as the multimodel median.

Wildfires of the historical period 2000–2019 were assessed using satellite imagery to derive thermal anomalies. A grid cell was considered affected if the temperature exceeded 300 K⁷³. The historical period is determined by the data availability through the MODIS satellite mission. The approach does not distinguish between intentional and unintentional fires, and the dataset captures gridpoint-specific annual maxima only.

Finally, in our preregistration, we broadly categorized tropical cyclones and European winter storms under the umbrella term ‘storms’. Typically, tropical cyclones prevail in tropical and subtropical regions, while our modelled winter storms are predominantly observed in Europe. Given their distinct geographical occurrences, the impacts of these two storm types can be considered additive or complementary. However, tropical cyclone impacts in higher latitudes, where storms often undergo extratropical transition (for example, Sandy in 2012, Dorian in 2019, Fiona in 2020), were included in the tropical cyclone category due to their origin. While this classification ensured consistency with our framework, modelling these exposures carries higher uncertainty compared with the tropics and subtropics. In addition, storm impacts are expressed relative to population size, which may lead to disproportionately high exposure percentages in regions with low population density compared with densely populated areas experiencing similar storm frequencies. We relied on historical records to assess the impacts of both storm hazards^74,75, and readers should interpret the results for higher latitudes with these considerations in mind.

Definition of exposed population

In this study, we defined ‘exposed population’ as the average annual proportion of a country’s total population exposed to a specific weather-related hazard within a given time period. An overview of time periods can be found in Supplementary Table 13. This was calculated by summing the number of individuals in each 150-arc-second grid cell who have experienced the hazard at least once during the study period and then dividing this sum by the country’s total population, based on the GPW dataset. Therefore, this metric is relative and does not reflect the severity of exposure or the potential for individuals to be repeatedly impacted by different events. In addition, in large countries such as the United States, different hazards may affect different regional populations (for example, wildfires on the West Coast versus tropical cyclones in the East) which, unfortunately, is not captured in our country-level aggregation. The exposed population is presented as a percentage of the total population, providing a standardized measure for comparative analysis across the 68 countries included in our study.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Online content

Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41558-025-02372-4.

Supplementary information

Supplementary Information^{(4.4MB, pdf)}

Supplementary Figs. 1–18 and Tables 1–13.

Reporting Summary^{(2.4MB, pdf)}

Acknowledgements

We thank H. Karami (University of Zurich) for managing the author list. I.R. and C.T.-E. were supported by ANR PICS; A.F.-B. was supported by Aarhus University Research Foundation grant AUFF-E-2019-9-13; P.M. was supported by Aarhus University Research Foundation grant AUFF-E-2019-9-2; R. Bardhan was supported by Africa Albarado Fund, Cambridge Africa ESRC GCRF, and UKRI ODA International Partnership Fund; J.P. Reynolds was supported by Aston University, and UK Research and Innovation (UKRI) under the UK government’s Horizon Europe funding guarantee EP/X042758/1; N.L. and R.M.R. were supported by Australian Research Council grant DP180102384, and John Templeton Foundation grant number 62631; O. Ghasemi was supported by Australian Research Council grant DP190101675; U.K.H.E. was supported by Australian Research Council grant FT190100708; D.D., A.G., D.G. and E.K. were supported by the Basic Research Program at the National Research University Higher School of Economics (HSE University); R.D. was supported by Bill and Melinda Gates Foundation grant OPP1144, a Cambridge Humanities Research Grant, CRASSH grant fund for climaTRACES lab, the Keynes Fund, the UKRI ODA International Partnership Fund, and the Quadrature Climate Foundation; T.C. and M.M. were supported by Boston University (Startup Funds); F.A. was supported by CNPq - INCT (National Institute of Science and Technology on Social and Affective Neuroscience, grant number 406463/2022-0); K.C.D. was supported by a COVID-19 Rapid Response grant from the University of Vienna, and Austrian Science Fund grant FWF I3381; C.L., J.P.N., E.P. and B.T. were supported by a COVID-19 Rapid Response grant from the University of Vienna, and Austrian Science Fund grants FWF I3381 and W1262-B29; R.M.A. was supported by Caltech RSI; C. Farhart was supported by Carleton College; C.L.-V. was supported by Cayetano Heredia University; H.H. and S. Kristiansen were supported by the Center for Climate and Energy Transformation, University of Bergen, Norway; C.G.B. and A.C.H.-M. were supported by Conacyt grant A1S9013; O.K. was supported by a Concerted Research Action grant from the Fédération Wallonie-Bruxelles (Belgium) (‘The Socio-Cognitive Impact of Literacy’); J.S. was supported by Core ETHZ funding and Swiss Agency for Development and Cooperation (SDC) grant 7F09521; E.A. was supported by Department of Economics, University of Warwick; H.G. was supported by the Department of Psychology, University of Sheffield; C.D. and F.G.R. were supported by Deutsche Forschungsgesellschaft grant RE 4752/1-1, and the David and Claudia Harding Foundation; I.M.A. was supported by the EDCTP2 Programme (TMA2020CDF-3171), and BMGF (INV075699); K.M.D. was supported by European Research Council Advanced Grant ‘Consequences of conspiracy theories - CONSPIRACY_FX’ grant 101018262; J.R. was supported by European Union’s Horizon 2020 Research and Innovation Programme under grant agreement number 101006436 (GlobalSCAPE); S. Meiler, C.M.K. and S.L. were supported by European Union’s Horizon 2020 research and innovation program grant agreement numbers 820712 (PROVIDE), 101073978 (DIRECTED) and 101081369 (SPARCCLE); G.H. was supported by Faculty Research Grant of City University of Hong Kong grant PJ9618021; O.S. and R.R.S. were supported by Fundação para a Ciência e a Tecnologia, UIDB/04295/2020 and UIDP/04295/2020; E.G. was supported by Government of Alberta Major Innovation Fund grant RES0049213; J.N. was supported by HELTS Foundation (USA); K. Breeden was supported by Harvey Mudd College; T.K.R. and K. Pštross were supported by the Institute of Communication Studies and Journalism, Charles University; H.F. was supported by Internal project costs IWM; M. Tanaka was supported by JST-RISTEX ELSI grant number JPMJRX20J3, and the Hitachi Fund Support for Research Related to Infectious Diseases; G.C. and E. Szumowska were supported by Jagiellonian University; M. Alfano and M. Ferreira were supported by John Templeton Foundation number 61378, John Templeton Foundation grant number 62631, and Australian Research Council DP1901015077; A. Krouwel was supported by Kieskompas.nl; M. Tsakiris was supported by the NOMIS Foundation; R.M. was supported by NOMIS Foundation/Leverhulme International Professorship Grant LIP-2022-001; T.K., K. Petkanopoulou and J.v.N. were supported by the NORFACE Joint Research Programme on Democratic Governance in a Turbulent Age, NWO, and European Commission through Horizon 2020 grant 822166; A.R. was supported by National Science and Technology Council, Taiwan (ROC) grant 112-2628-H-002-002 and 113-2628-H-002-018-; D.J. and A.D.W. were supported by Nicolaus Copernicus University; N.I. was supported by a Research grant from the College of Social Sciences, Kimep University; E.B., P.K. and A.Z. were supported by SNSF (VAR-EXP); O. Białobrzeska and M. Parzuchowski were supported by SWPS University; M.E. was supported by a School of Economics Interdisciplinary funding at University of Birmingham; C.A.J. and C.H.L. were supported by the School of Geography, Planning and Spatial Sciences, University of Tasmania; and the Centre for Marine Socioecology, University of Tasmania; E.J.N. and S.K.S. were supported by the School of Medicine and Psychology, Australian National University; M.D.M. was supported by School of Psychology and Public Health Internal Grant Scheme 2022; I.A. was supported by the School of Psychology, University of Sheffield; Beasiswa Pendidikan Indonesia Kemendikbudristek - LPDP provided by Balai Pembiayaan Pendidikan Tinggi (BPPT) Kemdikbudristek and LPDP Indonesia; R. Bhui was supported by the Sloan School of Management, Massachusetts Institute of Technology; O. Buchel was supported by Slovak Research and Development Agency (APVV), contract number APVV-22-0242; N.M.L. was supported by Social Sciences and Humanities Research Council grant number 430-2022-00711; M.P.-C. was supported by Statutory Funds from University of Silesia in Katowice; A.C.V. and L. Kojan were supported by OptimAgent (German Federal Ministry of Education and Research, Funding Code: 031L0299D) and the University of Lübeck; P.P. was supported by Swedish Research Council grant 2020-02584; L.S. was supported by Swiss Agency for Development and Cooperation (SDC) grant 7F09521; S.B. was supported by the Swiss Federal Office of Energy (SI/502093–01); J.L.G. was supported by Swiss National Science Foundation PRIMA Grant PR00P1_193128; V.C. was supported by Swiss National Science Foundation Postdoc Mobility Fellowship P500PS_202935, Harvard University Faculty Development Fund, and SPEED2ZERO Joint Initiative that received support from the ETH Board under the Joint Initiatives scheme; E.W.M. was supported by The HELTS Foundation; G.R. was supported by The São Paulo Research Foundation – FAPESP grant 2019/26665-5, and CNPq - INCT (National Institute of Science and Technology on Social and Affective Neuroscience, grant number 406463/2022-0); M. Facciani and T.W. were supported by USAID; F.M.-R. was supported by Universidad Peruana Cayetano Heredia; D.A. was supported by Universitas Islam Negeri Sunan Kalijaga; S.J. and S.J.M. were supported by the University of Bamberg; J.M.M. was supported by the University of Delaware; M.D. and I.W. were supported by the University of Lodz; A. Koivula and P.R. were supported by the University of Turku; M.B. and P.H. were supported by the University of Warsaw; A.P. and E.Z.-P. were supported by the University of Warsaw under the Priority Research Area V of the ‘Excellence Initiative – Research University’ programme; M.S.S. was supported by the University of Zurich/IMKZ; T. Ostermann and J.P. Röer were supported by the University research budget; A. Bajrami and R.T. were supported by University ‘Aleksandër Moisiu’, Durrës; S. Schulreich was supported by Universität Hamburg; L.S.K. was supported by the Victoria University of Wellington; H.K. was supported by Zhangir Kabdulkair.

Author contributions

V.C., S. Meiler, C.M.K., S.L., N.G.M., D.N.B., S.B., J.B., C.B., M.J., E.W.M., S. Mihelj, N.O., M.S.S. and S.v.d.L. conceptualized the study. V.C., S. Meiler, C.M.K. and S.L. curated the data. V.C. performed the analysis. O.L. and O. Ghasemi peer-reviewed the code. V.C., S.B., J.B., C.B., E.W.M., M.S.S. and the TISP Consortium acquired funding. V.C., S. Meiler, C.M.K., S.L., N.G.M., S.B., J.B., C.B., M.J., E.W.M., S. Mihelj, N.O., M.S.S., S.v.d.L. and the TISP Consortium conducted the investigation. V.C., S. Meiler, C.M.K., S.L., N.G.M., O.L., S.B., J.B., C.B., M.J., E.W.M., S. Mihelj, N.O., M.S.S. and S.v.d.L. discussed the design, methods and results. V.C. administered and supervised the project. V.C., S. Meiler, C.M.K., S.L., N.G.M., S.B., J.B., C.B., E.W.M., M.S.S. and the TISP Consortium collected data. V.C. wrote the original draft. V.C., S. Meiler, C.M.K., S.L., N.G.M., D.N.B., O.L., S.B., J.B., C.B., M.J., E.W.M., S. Mihelj, N.O., M.S.S., S.v.d.L. and the TISP Consortium reviewed and edited the paper draft.

Peer review

Peer review information

Nature Climate Change thanks Miaomiao Liu, Matto Mildenberger and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Funding

Open access funding provided by Swiss Federal Institute of Technology Zurich.

Data availability

The dataset on subjective attribution and policy support analysed during the current study is available in the Open Science Framework (OSF) repository at 10.17605/OSF.IO/5C3QD (ref. ⁷⁶). The dataset on exposed populations to extreme weather events generated and analysed during the current study is available in OSF at 10.17605/OSF.IO/G23A7 (ref. ⁷⁷).

Code availability

The analysis code is available in OSF at 10.17605/OSF.IO/G23A7 (ref. ⁷⁷).

Competing interests

The authors declare no competing interests.

Ethics statement

The questionnaire used for this study was considered exempt from full IRB review by the Harvard University Area Committee on the Use of Human Subjects in November 2022 (protocol number IRB22-1046).

Footnotes

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

A list of authors and their affiliations appears at the end of the paper.

Contributor Information

Viktoria Cologna, Email: viktoriacologna@gmail.com.

TISP Consortium:

Viktoria Cologna, Niels G. Mede, Oscar Lecuona, Sebastian Berger, John Besley, Cameron Brick, Marina Joubert, Edward W. Maibach, Sabina Mihelj, Naomi Oreskes, Mike S. Schäfer, Sander van der Linden, Nor Izzatina Abdul Aziz, Suleiman Abdulsalam, Nurulaini Abu Shamsi, Balazs Aczel, Indro Adinugroho, Eleonora Alabrese, Alaa Aldoh, Mark Alfano, Innocent Mbulli Ali, Mohammed Alsobay, R. Michael Alvarez, Tabitha Amollo, Patrick Ansah, Denisa Apriliawati, Flavio Azevedo, Ani Bajrami, Ronita Bardhan, Keagile Bati, Eri Bertsou, Rahul Bhui, Olga Białobrzeska, Michal Bilewicz, Ayoub Bouguettaya, Katherine Breeden, Amélie Bret, Ondrej Buchel, Pablo Cabrera Alvarez, Federica Cagnoli, André Calero Valdez, Timothy Callaghan, Rizza Kaye Cases, Sami Çoksan, Gabriela Czarnek, Ramit Debnath, Sylvain Delouvée, Lucia Di Stefano, Celia Diaz-Catalàn, Kimberly C. Doell, Simone Dohle, Karen M. Douglas, Charlotte Dries, Dmitrii Dubrov, Malgorzata Dzimińska, Ullrich K. H. Ecker, Christian T. Elbaek, Mahmoud Elsherif, Benjamin Enke, Matthew Facciani, Antoinette Fage-Butler, Zaki Faisal, Xiaoli Fan, Christina Farhart, Christoph Feldhaus, Marinus Ferreira, Stefan Feuerriegel, Helen Fischer, Jana Freundt, Malte Friese, Albina Gallyamova, Patricia Garrido-Vásquez, Mauricio E. Garrido Vásquez, Olivier Genschow, Omid Ghasemi, Theofilos Gkinopoulos, Jamie L. Gloor, Ellen Goddard, Claudia González Brambila, Hazel Gordon, Dmitry Grigoryev, Lars Guenther, Håvard Haarstad, Dana Harari, Przemysław Hensel, Alma Cristal Hernández-Mondragón, Atar Herziger, Guanxiong Huang, Markus Huff, Mairéad Hurley, Nygmet Ibadildin, Mohammad Tarikul Islam, Tao Jin, Charlotte A. Jones, Sebastian Jungkunz, Dominika Jurgiel, Sarah Kavassalis, John R. Kerr, Mariana Kitsa, Tereza Klabíková Rábová, Olivier Klein, Hoyoun Koh, Aki Koivula, Lilian Kojan, Elizaveta Komyaginskaya, Laura M. König, Lina Koppel, Kochav Koren, Alexandra Kosachenko, John Kotcher, Laura S. Kranz, Pradeep Krishnan, Silje Kristiansen, André Krouwel, Toon Kuppens, Claus Lamm, Anthony Lantian, Aleksandra Lazić, Jean-Baptiste Légal, Zoe Leviston, Neil Levy, Amanda M. Lindkvist, Grégoire Lits, Andreas Löschel, Alberto López Ortega, Carlos Lopez-Villavicencio, Nigel Mantou Lou, Chloe H. Lucas, Kristin Lunz-Trujillo, Mathew D. Marques, Sabrina J. Mayer, Ryan McKay, Taciano L. Milfont, Joanne M. Miller, Panagiotis Mitkidis, Fredy Monge-Rodríguez, Matt Motta, Zarja Muršič, Jennifer Namutebi, Eryn J. Newman, Jonas P. Nitschke, Ntui-Njock Vincent Ntui, Daniel Nwogwugwu, Thomas Ostermann, Tobias Otterbring, Myrto Pantazi, Philip Pärnamets, Paolo Parra Saiani, Mariola Paruzel-Czachura, Michal Parzuchowski, Yuri G. Pavlov, Adam R. Pearson, Charlotte R. Pennington, Katerina Petkanopoulou, Marija B. Petrović, Dinara Pisareva, Adam Ploszaj, Ekaterina Pronizius, Karolína Pštross, Katarzyna Pypno-Blajda, Diwa Malaya A. Quiñones, Pekka Räsänen, Adrian Rauchfleisch, Felix G. Rebitschek, Gabriel Rêgo, James P. Reynolds, Joseph Roche, Jan Philipp Röer, Robert M. Ross, Isabelle Ruin, Osvaldo Santos, Ricardo R. Santos, Stefan Schulreich, Emily Shuckburgh, Johan Six, Nevin Solak, Leonhard Späth, Bram Spruyt, Samantha K. Stanley, Noel Strahm, Stylianos Syropoulos, Barnabas Szaszi, Ewa Szumowska, Mikihito Tanaka, Claudia Teran-Escobar, Boryana Todorova, Abdoul Kafid Toko, Renata Tokrri, Daniel Toribio-Florez, Manos Tsakiris, Michael Tyrala, Özden Melis Uluğ, Ijeoma Chinwe Uzoma, Jochem van Noord, Iris Vilares, Madalina Vlasceanu, Andreas von Bubnoff, Izabela Warwas, Tim Weninger, Mareike Westfal, Adrian Dominik Wojcik, Ziqian Xia, Jinliang Xie, Ewa Zegler-Poleska, and Amber Zenklusen

Supplementary information

The online version contains supplementary material available at 10.1038/s41558-025-02372-4.

References

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Information^{(4.4MB, pdf)}

Supplementary Figs. 1–18 and Tables 1–13.

Reporting Summary^{(2.4MB, pdf)}

Data Availability Statement

The analysis code is available in OSF at 10.17605/OSF.IO/G23A7 (ref. ⁷⁷).

[CR1] 1.Seneviratne, S. I. et al. in Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al.) 1513–1766 (Cambridge Univ. Press, 2023). 10.1017/9781009157896.013

[CR2] 2.Newman, R. & Noy, I. The global costs of extreme weather that are attributable to climate change. Nat. Commun.14, 6103 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR3] 3.Byers, E. et al. Global exposure and vulnerability to multi-sector development and climate change hotspots. Environ. Res. Lett.13, 055012 (2018). [Google Scholar]

[CR4] 4.Howe, P. D. Perceptions of seasonal weather are linked to beliefs about global climate change: evidence from Norway. Clim. Change148, 467–480 (2018). [Google Scholar]

[CR5] 5.Otto, F. E. L. et al. Challenges to understanding extreme weather changes in lower income countries. Bull. Am. Meteorol. Soc.101, E1851–E1860 (2020). [Google Scholar]

[CR6] 6.Sisco, M. R. The effects of weather experiences on climate change attitudes and behaviors. Curr. Opin. Environ. Sustain.52, 111–117 (2021). [Google Scholar]

[CR7] 7.Anderson, B., Böhmelt, T. & Ward, H. Public opinion and environmental policy output: a cross-national analysis of energy policies in Europe. Environ. Res. Lett.12, 114011 (2017). [Google Scholar]

[CR8] 8.Schaffer, L. M., Oehl, B. & Bernauer, T. Are policymakers responsive to public demand in climate politics? J. Public Policy42, 136–164 (2022). [Google Scholar]

[CR9] 9.Keller, E., Marsh, J. E., Richardson, B. H. & Ball, L. J. A systematic review of the psychological distance of climate change: towards the development of an evidence-based construct. J. Environ. Psychol.81, 101822 (2022). [Google Scholar]

[CR10] 10.Marlon, J. R. et al. Hot dry days increase perceived experience with global warming. Glob. Environ. Change68, 102247 (2021). [Google Scholar]

[CR11] 11.Ogunbode, C. A., Doran, R. & Böhm, G. Individual and local flooding experiences are differentially associated with subjective attribution and climate change concern. Clim. Change162, 2243–2255 (2020). [Google Scholar]

[CR12] 12.Spence, A., Poortinga, W., Butler, C. & Pidgeon, N. F. Perceptions of climate change and willingness to save energy related to flood experience. Nat. Clim. Change1, 46–49 (2011). [Google Scholar]

[CR13] 13.Weber, E. U. What shapes perceptions of climate change? New research since 2010. WIREs Clim. Change7, 125–134 (2016). [Google Scholar]

[CR14] 14.Howe, P. D., Markowitz, E. M., Lee, T. M., Ko, C.-Y. & Leiserowitz, A. Global perceptions of local temperature change. Nat. Clim. Change3, 352–356 (2013). [Google Scholar]

[CR15] 15.Ettinger, J., Walton, P., Painter, J., Flocke, S. A. & Otto, F. E. L. Extreme weather events as teachable moments: catalyzing climate change learning and action through conversation. Environ. Commun.17, 828–843 (2023). [Google Scholar]

[CR16] 16.Hornsey, M. J., Harris, E. A., Bain, P. G. & Fielding, K. S. Meta-analyses of the determinants and outcomes of belief in climate change. Nat. Clim. Change6, 622–626 (2016). [Google Scholar]

[CR17] 17.Hoffmann, R., Muttarak, R., Peisker, J. & Stanig, P. Climate change experiences raise environmental concerns and promote Green voting. Nat. Clim. Change12, 148–155 (2022). [Google Scholar]

[CR18] 18.Konisky, D. M., Hughes, L. & Kaylor, C. H. Extreme weather events and climate change concern. Clim. Change134, 533–547 (2016). [Google Scholar]

[CR19] 19.Bergquist, P. & Warshaw, C. Does global warming increase public concern about climate change? J. Polit.81, 686–691 (2019). [Google Scholar]

[CR20] 20.Garside, S. & Zhai, H. If not now, when? Climate disaster and the Green vote following the 2021 Germany floods. Res. Polit.10.1177/20531680221141523 (2022). [Google Scholar]

[CR21] 21.Li, Y., Johnson, E. J. & Zaval, L. Local warming: daily temperature change influences belief in global warming. Psychol. Sci.22, 454–459 (2011). [DOI] [PubMed] [Google Scholar]

[CR22] 22.Sisco, M. R. & Weber, E. U. Local temperature anomalies increase climate policy interest and support: analysis of internet searches and US congressional vote shares. Glob. Environ. Change76, 102572 (2022). [Google Scholar]

[CR23] 23.Hazlett, C. & Mildenberger, M. Wildfire exposure increases pro-environment voting within Democratic but not Republican areas. Am. Polit. Sci. Rev.114, 1359–1365 (2020). [Google Scholar]

[CR24] 24.van Valkengoed, A. M. & Steg, L. Meta-analyses of factors motivating climate change adaptation behaviour. Nat. Clim. Change9, 158–163 (2019). [Google Scholar]

[CR25] 25.Brulle, R. J., Carmichael, J. & Jenkins, J. C. Shifting public opinion on climate change: an empirical assessment of factors influencing concern over climate change in the U.S., 2002–2010. Clim. Change114, 169–188 (2012). [Google Scholar]

[CR26] 26.Marquart-Pyatt, S. T., McCright, A. M., Dietz, T. & Dunlap, R. E. Politics eclipses climate extremes for climate change perceptions. Glob. Environ. Change29, 246–257 (2014). [Google Scholar]

[CR27] 27.Howe, P. D., Marlon, J. R., Mildenberger, M. & Shield, B. S. How will climate change shape climate opinion? Environ. Res. Lett.14, 113001 (2019). [Google Scholar]

[CR28] 28.Carmichael, J. T., Brulle, R. J. & Huxster, J. K. The great divide: understanding the role of media and other drivers of the partisan divide in public concern over climate change in the USA, 2001–2014. Clim. Change141, 599–612 (2017). [Google Scholar]

[CR29] 29.Brody, S. D., Zahran, S., Vedlitz, A. & Grover, H. Examining the relationship between physical vulnerability and public perceptions of global climate change in the United States. Environ. Behav.40, 72–95 (2008). [Google Scholar]

[CR30] 30.Palm, R., Lewis, G. B. & Feng, B. What causes people to change their opinion about climate change? Ann. Am. Assoc. Geogr.107, 883–896 (2017). [Google Scholar]

[CR31] 31.Marlon, J. R. et al. Detecting local environmental change: the role of experience in shaping risk judgments about global warming. J. Risk Res.22, 936–950 (2019). [Google Scholar]

[CR32] 32.Xia, Z. et al. A meta-analysis of the relationship between climate change experience and climate change perception. Environ. Res. Commun.4, 105005 (2022). [Google Scholar]

[CR33] 33.Wong-Parodi, G. & Garfin, D. R. Hurricane adaptation behaviors in Texas and Florida: exploring the roles of negative personal experience and subjective attribution to climate change. Environ. Res. Lett.17, 034033 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR34] 34.Whitmarsh, L. Are flood victims more concerned about climate change than other people? The role of direct experience in risk perception and behavioural response. J. Risk Res.11, 351–374 (2008). [Google Scholar]

[CR35] 35.van der Linden, S. On the relationship between personal experience, affect and risk perception: the case of climate change. Eur. J. Soc. Psychol.44, 430–440 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]

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PERMALINK

Extreme weather event attribution predicts climate policy support across the world

Viktoria Cologna

Simona Meiler

Chahan M Kropf

Samuel Lüthi

Niels G Mede

David N Bresch

Oscar Lecuona

Sebastian Berger

John Besley

Cameron Brick

Marina Joubert

Edward W Maibach

Sabina Mihelj

Naomi Oreskes

Mike S Schäfer

Sander van der Linden

Abstract

Main

Current study

Support for climate policies

Fig. 1. Global evidence of the support for climate policies.

Subjective attribution

Fig. 2. Subjective attribution of extreme weather events to climate change (mean index) over the past decades.

Exposed population and policy support

Fig. 3. Exposed population across countries over the past few decades.

Fig. 4. Weighted blockwise multilevel models predicting climate policy support.

Fig. 5. Interactions between subjective attribution and exposed population to extreme weather events on climate policy support.

Interaction effects with income and residence area

Discussion

Methods

Dataset

Sample and weighting

Main measures included in the questionnaire

Climate policy support

Subjective attribution

Analyses

Impact model CLIMADA

Exposure

Hazards

Definition of exposed population

Reporting summary

Online content

Supplementary information

Acknowledgements

Author contributions

Peer review

Peer review information

Funding

Data availability

Code availability

Competing interests

Ethics statement

Footnotes

Contributor Information

Supplementary information

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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