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. 2024 Nov 30;14:29817. doi: 10.1038/s41598-024-80139-1

Identification of climate change hotspots in the Mediterranean

Georgia Lazoglou 1, Alexandros Papadopoulos-Zachos 2, Pantelis Georgiades 1, George Zittis 1, Kondylia Velikou 2, Errikos Michail Manios 2, Christina Anagnostopoulou 2,✉
PMCID: PMC11608228  PMID: 39616216

Abstract

The Mediterranean region has long been identified as a climate change hotspot. However, within the Mediterranean, there are smaller sub-areas that exhibit a higher risk of climate change and extremes. Previous research has often focused on indices based on mean climate values, yet extremes are typically more impactful on humans and ecosystems. This study aims to identify the most vulnerable sub-areas of the Mediterranean as climate change hotspots using two indices: the newly introduced Mediterranean Hotspot Index (MED-HOT) and the well-defined Regional Climate Change Index (RCCI). The MED-HOT focuses on extreme high maximum and minimum temperatures, rainfall, and drought, while RCCI assesses changes in mean climate conditions. By combining these indices, we provide an identification of Mediterranean hotspots, capturing both mean climate shifts and extremes. The spatiotemporal variation of both indices across the Mediterranean region is presented and the 20 subregions are categorized into distinct groups. The results reveal that the southeastern Mediterranean is at high risk according to both indices. Additionally, southern Italy is identified as high risk due to changes in mean climate (RCCI), while the northern part is at risk due to extreme events (MED-HOT). The Iberian Peninsula and Greece are also highlighted as vulnerable areas requiring extra attention.

Keywords: Hotspot, Mediterranean, MED-Hot Index, Extremes

Subject terms: Climate sciences, Environmental sciences

Introduction

The Mediterranean has been identified as one of the most responsive regions to global climate change1, with observed warming expected to continue at a greater rate than the global average2. Consequently, the Mediterranean is well-defined as a hotspot area according to observations and future projections3,4. This characterization was first underscored by the application of the Regional Climate Change Index (RCCI)5, designed to identify regions most responsive to climate change through assessing future changes in mean conditions and interannual variability of precipitation and near-surface air temperature.

Due to the interest in this area, several studies have been conducted to assess past and future changes in crucial climate parameters. The annual precipitation of the Mediterranean area is expected to show a decrease of about 4% for every Inline graphic increase in global temperature. This decrease is expected mainly in the summer season for the northern regions of the Mediterranean basin and across all seasons in the central and southern regions6. Other studies mention a decrease in precipitation for all seasons, resulting in a reduction of annual precipitation that can reach Inline graphic of global warming in parts of the Balkans, Anatolia, and the Iberian Peninsula7. This decline in precipitation8,9, along with the significant rise in temperatures7, has led to higher evaporative demand, increasing drought severity10. Future climate change scenarios from various model experiments also agree on an increased frequency and severity of droughts in the Mediterranean basin11,12.

Another crucial climate parameter showing a significant increasing trend, particularly during the summer months, is extreme high minimum temperatures, which have broad impacts across various sectors. For instance, in the Eastern Mediterranean, specifically in Israel, indicators related to high minimum temperatures have shown a marked increase, rising by Inline graphicC per decade from 1988 through 201713. Similarly, for a longer period (1961–2020), a significantly increasing trend in extreme minimum temperatures has been observed in the Montenegro region, another part of the Mediterranean14. These changes are particularly important as they often represent nighttime temperatures, which can contribute to heat stress and have direct consequences for human health and ecosystems15.

Apart from the changes in mean climate conditions, another important aspect of global warming is the increase in the frequency and intensity of extreme weather events. Xu et al.16 introduced the Regional Extreme Climatic Change Index (RECCI), a global index that combines seven extreme climatic indices, focusing on changes in the frequency, intensity, and interannual variability of temperature, precipitation, and wind speed extremes. According to this index, the Mediterranean shows a comparatively lower response to climate change than other regions, such as Alaska or the Amazon Basin. However, despite this global assessment, several studies have emphasized the need for a deeper analysis of extreme events specifically within the Mediterranean. Frequent and intense occurrences of extreme events in this region pose significant risks to key sectors, including agriculture17, health18, and tourism19, underscoring the region’s specific vulnerabilities to climate change. For instance, over the next century, a strong increase in the frequency and intensity of heatwaves are expected, with this outcome being robust across most climate models and scenarios20,21. Extreme rainfall has shown an upward trend in numerous northern Mediterranean regions and is expected, with high confidence, to increase further and probably be accompanied by an upsurge in flash floods, while no significant change is expected in the south6,22. Despite the overall uncertainty, the absolute rainfall maxima (e.g. 1-in-50-years daily extremes) are expected to become more intense throughout the region23.

Taking into account all the above, it is highly important to conduct a deeper analysis of such hotspot areas, where, on one hand, the effects of climate change are more pronounced, and on the other hand, the capacity to adapt is not equally distributed across the entire region. Hence, the present study aims to identify the most vulnerable sub-regions within the Mediterranean area, focusing on areas responding to either mean climate values or extreme events. This approach provides critical insights for future research, highlighting specific locations that require increased attention due to their susceptibility to climate change impacts. However, several studies assess the behaviour of climate parameters in the Mediterranean and mention parts of the basin where historical changes are more intense, a more holistic and rigorous analysis is still missing, according to the authors’ knowledge.

The primary objective of the present research is to identify the most vulnerable sub-areas in the Mediterranean using two climate indices, the newly introduced Mediterranean Hotspot Index (MED-HOT) and the well-defined Regional Climate Change Index (RCCI)5. While the RCCI index is widely recognized for identifying climate hotspots based on changes in mean climate parameters, it has a key limitation: it does not account for changes in climate extremes—both in terms of frequency and intensity—which are critical drivers of climate impacts, particularly in the Mediterranean basin. Climate extremes, such as heatwaves, intense rainfall, and droughts, often have a much greater impact on ecosystems, economies, and societies than changes in average climate conditions alone. To address this gap, we developed the MED-HOT index, which specifically focuses on changes in these climate extremes. By integrating the MED-HOT index with the RCCI index, our approach provides a more comprehensive assessment of climate vulnerabilities in the region. The RCCI index highlights regions affected by broader shifts in mean climate, while the MED-HOT index emphasizes areas where extreme weather events are becoming more frequent and severe. This dual approach ensures that we capture all critical hotspots, including those where the mean climate may not show significant change but where extreme events have substantial impacts. The inclusion of the MED-HOT index significantly enhances our analysis, offering a deeper understanding of vulnerabilities in the Mediterranean region by accounting for both mean climate changes and the increasing relevance of climate extremes. The MED-HOT index incorporates four key indicators of extreme events typically affecting the region: extreme maximum and minimum temperatures, intense precipitation, and droughts. By combining both indices, this study identifies the most vulnerable sub-areas, allowing for targeted, region-specific analyses. This, in turn, can help develop more effective strategies to mitigate the regional impacts of global warming and the associated socio-economic consequences.

Results

Classification of MED-HOT index and its spatial analysis

The MED-HOT index is designed to assess climate vulnerabilities in the Mediterranean region by integrating changes in the frequency and intensity of four extreme climate indicators: extreme maximum temperature (TX90), extreme high minimum temperature (TN90), extreme precipitation (P95), and consecutive dry days (CDD). This index reflects the primary climate challenges faced in the region, including heatwaves and droughts. To compute the MED-HOT index, the analysis divides the available data into two periods: the early historical period (1981–2000) and the late historical period (2001–2022). For each index, the frequency and intensity of extreme events are calculated and then normalized. The final MED-HOT index is derived by summing the normalized differences in frequency and intensity across the four indicators, providing a comprehensive measure of climate vulnerability. While the theoretical maximum value of 8 can be achieved if all eight components reach their normalized maximum value of 1, this scenario is highly improbable in practice. Each grid point is analyzed independently, and it is unlikely for a single grid point to exhibit the maximum values for all eight components simultaneously. For instance, a grid experiencing the maximum value of extreme temperature (TX90) often does not coincide with the grid experiencing the maximum value of the P95 precipitation index. These extremes typically occur under different climatic regimes and locations. Additionally, both the frequency and intensity of each indicator are analyzed separately, making it even rarer for a grid point to exhibit the maximum values for both components at the same time. For example, a location experiencing the most frequent extreme precipitation events may not record the highest precipitation intensity simultaneously. This further decreases the chances of reaching the theoretical maximum. As a result, the observed MED-HOT values across the Mediterranean range from zero to three. Nevertheless, these values still represent significant climate extremes and should not be interpreted as indicating only moderate impacts of climate change (Fig. 1).

Fig. 1.

Fig. 1

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Spatial variability of MED-HOT index classes.

It’s important to clarify that the MED-HOT index is comparative, following the definition used in Giorgi’s RCCI index. A lower value of the MED-HOT index does not necessarily indicate a small absolute change within a grid point. Instead, it reflects a smaller climate response relative to other grid points in the study area. This approach highlights the relative changes in climate response across the region, rather than directly measuring the absolute magnitude of change.To better understand the index’s spatial distribution, values have been classified into five categories (Fig. 1). Areas with index values below one are categorized as the first class, indicating minor changes and non-hotspot areas. The remaining areas are divided into four additional categories, each with a 0.5 range. This classification allows us to effectively capture the variability of both indices while minimizing the introduction of noise.

The classification of the MED-HOT index is depicted in Fig. 1. The western part of the Iberian Peninsula, along with the majority of central and eastern parts of North Africa, fall within the first class, indicating relatively low MED-HOT values compared to other areas. The next two classes, with MED-HOT values from 1 to 2, are the most prevalent. Almost all grids located in the western part of the studied area (latitudes lower than 10 degrees north) belong to these two classes. Additionally, a significant extent of the Balkan Peninsula, as well as the central and northeastern parts of the studied area, fall into these two classes. In the last two classes, where the index reaches its maximum values, the areas become more specific. Particularly, the eastern regions of Spain, the Dalmatian coast, the Alps, limited regions in the eastern Mediterranean, and areas of northern Greece and Turkey exhibit the highest values of the MED-HOT index.

A more focused analysis resulting in selecting the sub-areas with the highest response to climate change within the Mediterranean is presented in Fig. 2. To ensure an accurate selection a two-step analysis was made.

Fig. 2.

Fig. 2

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(A) Mean value of the MED-HOT index for the 30 Mediterranean sub-areas. (B) Mean MED-HOT index values multiplied with the percentile of gridpoints belonging into the two greatest classes for each sub-area. The blue circles highlight the sub-areas with the highest MED-HOT index values, indicating climate hotspots.

Firstly, the mean value of the MED-HOT index for each of the 20 sub-regions was calculated (Fig. 2A). In the second step, a deeper procedure was applied, with the results shown in Fig. 2B. Specifically, the number of grid points in each sub-area was computed, along with the number of grids falling within the highest two classes of the index. Next, the percentile rank of each grid box within these high-value classes was determined. This percentile rank was then multiplied by the mean value of the index for each grid box. This method ensures that sub-areas with both very high and very low MED-HOT values give more weight to significant changes. As a result, the overall behavior is not obscured by averaging, and significant changes are highlighted.

The results reveal that the areas with the highest index values are the southeastern coast of the Mediterranean (no. 10 Fig. 7—Israel area—ISR), the northern part of Greece (no 19 Fig. 7—GR), and the part of Northern Italy and the Dalmatian coast (NIT). The index values for all three hotspot areas are very close, with the highest recorded in the ISR sub-area.

Fig. 7.

Fig. 7

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Division of the Mediterranean into 30 equal-sized subregions.

Classification of RCCI index and its spatial analysis

The RCCI is classified into five distinct classes, with higher values indicating regions experiencing more pronounced changes compared to others within the study area.

Figure 3 illustrates the spatial distribution of the classified RCCI index. According to this index, regions exhibiting a low response to climate change include parts of the Balkan Peninsula, northwest Africa, central Italy, and the southeastern domain. Areas displaying moderate change are predominantly observed across much of northern Africa, with notable concentrations around regions exhibiting the highest RCCI values, such as the Iberian Peninsula, Alps, Dalmatian coast, northern Greece, Cyprus, and adjacent coastal areas.

Fig. 3.

Fig. 3

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Spatial variability of RCCI index classes.

To better delineate hot spot areas according to the RCCI index, we conducted a similar extensive analysis as followed before for the MED-HOT index (Fig. 4). According to this index-secondary analysis (Fig. 4B), the three sub-areas which should be characterised as hot spots are the box that includes the island of Cyprus (no. 15 Fig. 7—CY), the one where the Iberian Peninsula is located (no. 16 Fig. 7—IB) and the sub-area including southern Italy and parts of Sicily (no. 18 Fig. 7—SIT). It is worth mentioning that the sub-area where North Greece belongs can be characterized as a hotspot area according to the mean value of the RCCI index, as it has the same value (12.1) as the next sub-area of South Italy. However, according to the secondary analysis where the number of the grid points with very high values have an extra weight, the area of South Italy has a more significant response to climate change.

Fig. 4.

Fig. 4

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(A) Mean value of the RCCI index for the 30 Mediterranean sub-areas. (B) Mean RCCI index values multiplied with the percentile of gridpoints belonging into the two greatest classes for each sub-area. The blue circles highlight the sub-areas with the highest RCCI index values, indicating climate hotspots.

Definition and assessment of hotspot areas

Six hot spot regions can be identified by combining the results of the MED-HOT and RCCI indices. According to historical changes, these regions are considered the most vulnerable to climate change in the Mediterranean. Figure 5 displays these areas: hotspots identified by the RCCI index are shown in yellow, while those identified by the MED-HOT index are shown in orange. Each hotspot area is given an acronym, mainly based on the country that covers the largest part of the area. The identified hotspot regions that are the most vulnerable areas due to recorded changes in mean temperature and precipitation values are: ‘IB’ for the Iberian Peninsula, ‘SIT’ for south Italy, and ‘CY’ for Cyprus. North Italy (‘NI’), Greece (‘GR’), and Israel (‘ISR’) are hot spots due to changes in extreme climate events. For each of these sub-areas, an analysis of the contribution of each factor to the final value of the respective index (MED-HOT or RCCI) was conducted and is presented in Fig. 6.

Fig. 5.

Fig. 5

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Hotspot areas according to the MED-HOT (orange) and RCCI (yellow) indices.

Fig. 6.

Fig. 6

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Contribution (%) of the final value of the MED-HOT and RCCI indices for eight sub-areas.

The relative (%)contributions of the indices to the MED-HOT index value are illustrated in the top panel of Fig. 6. The results indicate that the change in the maximum temperature is the dominant factor, exceeding 25% while extreme rainfall and drought indices contribute less than 20% in the three selected areas. For Israel and Greece, it appears that the hot spot definition is based on changes in the maximum values of extreme temperatures (TX90 and TN90), leading to warmer days and nights. In Northern Italy (NIT), the hot spot region is characterized by a 51.5% change in the frequency and intensity of maximum temperatures, resulting in both more frequent and higher maximum temperatures.

However, when calculating the RCCI index for these areas, it is revealed that changes in average precipitation during either the wet or dry season account for more than 50% of the index (Fig. 6—RCCI). Specifically, in Greece, the most significant factor is the change in average rainfall during the dry season. Conversely, in Israel, the variance in average precipitation throughout the year contributes nearly 50% to the index. In northern Italy (NI), the index is characterized by changes in both the average precipitation and its variance during the wet season.

The plot for the RCCI in the bottom panel of Fig. 6 illustrates the contribution of various factors during wet and dry periods. The highest contribution to the definition of hot spot areas according to the RCCI index comes from changes in rainfall, which contrasts with the results of the MED-HOT index. The exception is the area of Cyprus, where there seems to be a balance between historical changes in average temperature and precipitation. In the Iberian (IB), the significant factor is the change of the variance in precipitation during the dry period of the year (20.8%), while in southern Italy (SIT), changes in rainfall and its variance during the wet period are the key characteristics of the hot spot region. For Cyprus (CY), the region is mainly characterized by changes in temperature variance (20.5%) during the dry period, and the RWAF factor (19%), representing the change in regional mean surface air temperature relative to the Mediterranean average temperature change for the wet period.

When calculating the MED-HOT index for these three regions (Fig. 6—MED-HOT), distinct factors contribute significantly. The common characteristic across all three regions is the notable change in the temperature indices (higher than 70%). However, in Cyprus (CY), changes in maximum minimum temperatures combined with alterations in the drought index account for 50% of the MED-HOT index. In IB, the second primary contributing factor is the change in the drought index, while for southern Italy (SI), the index is largely influenced by changes in the frequency of extreme precipitation.

Discussion and conclusions

The Mediterranean region has been recognized as a significant climate change hotspot through both observational and model-based studies3,24. Nevertheless, there remains a gap in considering holistic approaches that can pinpoint the most vulnerable sub-areas within the Mediterranean. Identifying these sub-areas is essential for targeted impact studies as it provides additional insights about the regions that require heightened attention. Furthermore, as global warming drives an increase in the frequency and intensity of extreme events25, it is essential to identify the most affected areas and develop strategies to mitigate the associated risks26. This underscores the necessity for a more localized definition of hotspots to enhance targeted interventions and adaptive measures.

This study aims to identify climate hotspot regions within the Mediterranean considering historical changes in mean and extreme climate conditions. For this reason, two climate indices are combined. Firstly the newly developed MED-HOT index encompasses four indicators focused on extreme temperature and precipitation values. The selection of these specific indicators is based on the observed increase in extreme events in the Mediterranean area, particularly related to heatwaves, extreme rainfalls, and droughts27.It is important to note that while the MED-HOT index is tailored to the Mediterranean—hence its name—applying it to other regions is possible, but it should be done with caution. Regions that experience different types of extreme events, such as extreme cold, may require additional or modified indicators to assess climate vulnerabilities accurately. This specificity highlights both the strengths and the potential limitations of the MED-HOT index.

In addition to this new index, our analysis incorporates the Regional Climate Change Index (RCCI), which has been extensively used for identifying global hotspot areas by examining changes in mean temperature and precipitation5. By combining both the MED-HOT index and RCCI, we aim to provide a comprehensive understanding of climate vulnerabilities, ensuring that both mean climate changes and the impacts of extreme events are adequately represented in our identification of hotspot regions.

According to our findings, six sub-areas in the Mediterranean can be characterized as hotspots due to their heightened exposure to changing climate conditions. The designation of ‘hotspot’ for some of there sub-areas arises from changes in extreme conditions (MED-HOT index), while for others it is based on changes in mean conditions and variability (RCCI index).

The Eastern Mediterranean is a sub-region that needs significant attention as it is affected by changes in both extreme and mean climate conditions. In particular, the area of Israel appears to be significantly sensitive to increases in minimum temperatures. High nightime temperatures pose a threat to a variety of sectors including human health and also crop sustainability. For example, in Israel, faster increases in night temperatures compared to daytime temperatures impact crop production negatively28. Furthermore, climate change projections for Israel indicate temperature increases and precipitation declines, leading to farmers adapting by partial abandonment of agricultural lands and focusing more on controlled environment crops, ultimately affecting agricultural production and prices29. Cyprus experienced notable changes in mean values of temperature and precipitation30. These changes lead to a transition to drier conditions, exacerbating water stress on the island31. Moreover, extreme temperatures associated with climate change result in heat-related mortality, with a rapid increase in deaths for each degree rise in temperature32,33. This is in accordance with other studies that have found that the Eastern Mediterranean and the Middle East (EMME) are greatly affected by climate change due to the increasing frequency and intensity of droughts and hot weather conditions34. Specifically, the EMME region is warming almost twice as fast as the global average and other inhabited parts of the world35.

Another area characterized as a hotspot, primarily due to changes in extreme events, is the northern part of Greece. This finding corroborates previous studies that have noted significant warming in Greece over the past three decades, particularly during the summer36. The rise in maximum temperatures in Greece, particularly in the southern regions, as highlighted by recent studies37, can have significant implications for agriculture and forests. For instance, according to climate projections, a significant rise in the number of days with extreme fire weather conditions is expected38. Additionally, climate change scenarios predict an increase in hot days and tropical nights, coupled with a decrease in frost days and wet days, which could adversely affect crops sensitive to water and temperature stress, such as wheat, tomatoes, cotton, and grapes39. Furthermore, historical data analysis reveals a strong correlation between forest fire activity and air temperature, with a notable increase in the number of fires and burnt areas associated with higher temperatures. This underscores the critical role of maximum temperatures in the occurrence and spread of fires.

Italy has also been impacted exceptionally by recent changes in climate, with the northern part being more vulnerable due to extreme conditions. The Alpine area contributes significantly to the characterization as a hotspot, as it has been shown that the response to climate change is intense40. The southern part of Italy is one of the six hotspot regions characterized by pronounced changes in mean climate conditions. Studies in the region highlight decreasing trends in annual and winter-autumn rainfall alongside an increasing trend in extreme rainfall events, impacting water resources management and extreme phenomena like droughts and floods41. However, other studies have also identified this area as significantly vulnerable to changes in extreme events. For example, Faggian42 observed a warming trend across Italy, with a notable increase in extremely hot days in the southern part of the country.

In the western part of the Mediterranean, a region characterized as the hot spot is the Iberian Peninsula, mainly due to the changes in mean conditions. This is in agreement with43, who assessed the Iberian Peninsula as an area susceptible to changes in precipitation and temperature, with a high risk of experiencing extreme temperatures and summer heatwaves44. These changes are likely to exacerbate water stress, impacting groundwater levels, agriculture, and ecosystem services in the IB region43

This research offers a pivotal map that highlights the most vulnerable areas in the Mediterranean, providing essential guidance for future research on climate change impacts. By introducing the MED-HOT index alongside the established RCCI index, this study identifies hotspot areas based on both extreme and mean climate responses, offering a more comprehensive assessment of regional vulnerability. The results can serve as a valuable tool for prioritizing areas that require focused attention, particularly in the context of climate adaptation and mitigation strategies. Moreover, the potential application of the MED-HOT index beyond the Mediterranean could provide further support for decision-making in various sectors, enabling more informed and strategic planning to address the global challenges posed by climate change.

Data and methods

Data

In the present study, we utilize daily temperature and precipitation values derived from the ERA5 reanalysis database45. The assessed data, available at a spatial resolution of about 25 km, cover the period from 1981 to 2022. It should be noted that only continental grid points are assessed.

Changes in climate parameters between early and late historical periods were calculated to identify hotspot areas. The available 40-year period of the ERA5 used dataset was divided into two equal 20-year subperiods: the early historical period (1981–2000) and the late historical period (2001–2022). This approach follows established climatological methods, where 20-year periods are typically used to capture climate trends and variability.

The analysis was conducted for the broader Mediterranean area, spanning from − 11 to 41 degrees longitude and from 24 to 51 degrees latitude. As the Mediterranean does not exhibit the same degree of climate variability as observed on a global scale in the referred RCCI study5, we adopted a systematic approach, dividing the region into 30 equal-sized sub-areas (Fig. 7). This division allowed us to capture regional climate variability effectively without overcomplicating the analysis. This method allowed us to maintain a balance between capturing regional climate variability and avoiding the creation of boxes that might encompass areas with significant differences in climate characteristics. Regions that fall outside the Mediterranean were omitted (Boxes 1–5, 21, 25–26, and 29–30).

Following Giorgi’s approach, we opted to define sub-areas (boxes) rather than adopting a grid-point approach. This decision was made to provide a consistent and organized overview of regional climate trends. The sub-area method ensures clarity while capturing broader regional patterns, making it easier to analyze both mean and extreme climate indicators without introducing unnecessary spatial noise or complexity. Although a grid-point approach could offer finer spatial details, it risks identifying localized extremes that may not reflect broader trends. The sub-area method, therefore, balances the need for clarity, statistical robustness, and comparability across the Mediterranean region. Researchers seeking more localized insights can still refer to the grid-point maps (Figs. 1 and 3) which retain finer spatial detail and can be used for more focused, grid-level investigations in future studies.

Methodology

To identify the vulnerable sub-areas within the Mediterranean Basin, we developed the Mediterranean Hotspot Index (MED-HOT), which emphasizes extreme values. To evaluate and compare this new index, we also conducted an analysis using the established RCCI index introduced by5, which utilizes mean values of climate parameters and their variability. A comprehensive analysis of the new MED-HOT index and a general description of the Regional Climate Change Index (RCCI) are presented.

MED-HOT index

The MED-HOT index integrates changes in the frequency and intensity of four key extreme climate indicators, as defined by the Expert Team on Climate Change Detection and Indices (ETCCDI)46. These indicators capture the primary climate extremes that significantly affect the Mediterranean region. Specifically, two temperature indices (TN90 and TX90) represent extreme minimum and maximum temperatures, one precipitation index (P95) reflects intense rainfall events, and the Consecutive Dry Days (CDD) index measures meteorological drought severity. The CDD index is both easy to calculate and straightforward to interpret, offering an effective measure of drought by focusing on the duration of dry periods, making it particularly relevant for understanding prolonged dry spells in the Mediterranean climate. All four indices are well-established within the ETCCDI framework, ensuring methodological consistency. Together, they provide clear, robust insights into the region’s climate vulnerabilities, addressing key concerns such as heatwaves, heavy rainfall, and drought.

For the final computation of the MED-HOT index, the mean frequency and intensity changes of each of these indices are calculated for the two sub-periods: 1981–2000, serving as the early historical reference period, and 2001–2022, designated as the late historical period. These changes are then normalized accordingly and integrated into the final MED-HOT index calculation.

The following section provides a detailed explanation of the extreme indices used in this analysis, along with the approach employed to calculate their frequency and intensity.

  • TX90: This index assesses extreme events related to maximum temperature. Frequency is defined as the number of days when the maximum temperature exceeds the 90th percentile threshold, calculated from the early period (1981–2000) time-series, using a 5-year centred moving average. Intensity is indicated by the mean temperature on these days.

  • TN90: This index follows the same procedure as TX90 but is calculated using the daily minimum temperature values.

  • P95: This index assesses extreme precipitation events. Frequency is determined by counting the number of days with daily precipitation exceeding the 95th percentile of the early period. Intensity is defined as the average precipitation amount on days when precipitation surpasses this threshold.

  • CDD: This index measures consecutive dry days. Frequency is determined by counting episodes where total precipitation over a minimum of five consecutive days is less than 0.1 mm. Intensity is calculated as the total number of days in such episodes divided by the number of episodes, representing the average duration of dry spells.

The analysis begins by calculating the intensity and frequency of the four extreme indices (TX90, TN90, P95, CDD) for both the early and late historical periods. Next, the difference in frequency and intensity (DF and DI) for each one of the indices is calculated by subtracting the early historical from the late historical values and then dividing by the early historical ones. To enable comparative analysis and interpretation across the indicators and periods, these differences are normalized by dividing with the maximum value across all grid points. The MED-HOT index is derived by summing these normalized differences as follows:

graphic file with name M4.gif 1

where i (Inline graphic) represents the four extreme climatic indices (TX90, TN90, P95, CDD) respectively.

RCCI index

The Regional Climate Change Index (RCCI), as defined by Giorgi et al.5, evaluates regional climate change impacts by analyzing mean precipitation and surface air temperature changes, along with variations in interannual variability. This index, identifies hotspots, regions most vulnerable to climate change, through comparative analysis across diverse global simulations. In our study, this analysis is conducted across grid points in the Mediterranean region. The RCCI is derived for each grid point by aggregating four parameters: the Regional Warming Amplification Factor (RWAF), denoting the change in regional mean surface air temperature in relation to the corresponding global change (here instead of global we use the change in the average Mediterranean temperature), the change in mean regional precipitation (Inline graphic), the change in interannual variability of regional surface air temperature (Inline graphic) and the change in the interannual variability of regional precipitation (Inline graphic). Each parameter is weighted by an ‘n’ value based on the magnitude of observed change, ranging from 0 to 4. This weighting system ensures significant contributions from factors with substantial changes while mitigating the impact of those with minimal variations. For a comprehensive understanding of threshold determination and ‘n’ selection, refer to Giorgi et al.5. Calculations are carried out separately for wet and dry seasons, defined as October to March and April to September, respectively, for the Mediterranean region. The final index value is the sum of the individual seasonal calculations.

Acknowledgements

This research was supported by the PREVENT project that has received funding from the European Union’s Horizon Europe Research and Innovation Program under Grant Agreement No. 101081276.

Author contributions

G.L. and C.A. conceived and designed the project. P.G. and G.L. worked with the data collection and its quality control. G.L., A.P.Z. and P.G. performed calculations, data analysis and interpretation of the results. C.A. and G.Z. provided general scientific input. G.L. lead the manuscript preparation. All authors assisted in manuscript writing, reviewed the results and approved the final version.

Data availability

The datasets generated and analysed during the current study are available in the Zenodo repository, DOI: 10.5281/zenodo.12566576.

Declarations

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

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

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Associated Data

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

Data Availability Statement

The datasets generated and analysed during the current study are available in the Zenodo repository, DOI: 10.5281/zenodo.12566576.

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