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. 2026 Feb 16;198(3):228. doi: 10.1007/s10661-026-15074-x

Historical trends reveal significant increase in hot-dry extremes in Mexico’s Bajío region

Fasil M Rettie 1,✉, Tek B Sapkota 2, Simon Fonteyne 2, Mekides W Gardi 1, Tesfay S Sida 1
PMCID: PMC12909320  PMID: 41699147

Abstract

This study provides a first-of-its-kind comprehensive analysis of temperature and precipitation extreme trends in the Bajío region of Mexico over the past four decades, utilizing high spatial resolution reanalysis data. Our findings reveal a compelling and consistent warming trend, characterized by a significant increase in warm days, warm nights, warm spell duration, and summer days, alongside a notable decrease in cold extremes. This widespread warming has profound implications for agricultural sustainability, primarily through increased heat stress and evapotranspiration. In contrast, precipitation trends exhibit a complex and spatially variable picture, with a predominant decrease in intense 5-day precipitation, consecutive wet days, and total wet-day precipitation, while 1-day extremes and the frequency of heavy rainfall days show more mixed or stable patterns. The exploration of a large set of global drivers provides a broader context for understanding these regional changes. Despite some limitations, such as potentially unbalanced data in composite analysis and the masking of certain extremes by reanalysis data, this research is crucial for understanding regional climate impacts and informing future studies. The observed shifts necessitate targeted adaptation strategies for water resource management and agricultural planning to ensure the long-term sustainability of the Bajío region.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10661-026-15074-x.

Keywords: Composite analysis, Extremes indices, Precipitation, Temperature

Introduction

The increasing frequency and severity of climate extremes, such as heat stress, droughts, and floods, pose significant risks to human and natural systems globally (Asseng et al., 2015; Calvin et al., 2023; Zhao et al., 2017; Zhou et al., 2023). These extreme events are particularly detrimental to agricultural production, where the viability of certain crops is at risk due to shifts in temperature and water availability, leading to potential compromises in food security (Calvin et al., 2023; Vogel et al., 2019). Mexico is among the countries most vulnerable to the impacts of climate change, with its agricultural sector facing substantial challenges (De Lima et al., 2023).

The Bajío region, located in central Mexico and encompassing parts of Guanajuato, Jalisco, and Michoacán, is a key agricultural hub of the nation’s agricultural output (Van Den Broeck et al., 2013). This “breadbasket” region, renowned for its fertile soils and extensive irrigation infrastructure, contributes significantly to national food security and rural livelihoods. It is a major producer of staple grains, including 39% of the national barley, 29% of sorghum, 25% of maize, and 22% of wheat, as well as high-value export crops such as avocados, berries, and agave (https://infosiap.siap.gob.mx/gobmx/datosAbiertos.php). With an average cultivated area of about 16 hectares per farm household, sorghum and maize attain a productivity of 8 to 12 t/ha, while wheat and barley yields reach 5 to 7 t/ha (Van Den Broeck et al., 2013).

The Bajío region is increasingly susceptible to climate variability. Recent decades have seen pronounced shifts in precipitation patterns, including delayed rain onset, shorter rainy seasons, and more frequent dry spells, directly impacting agricultural productivity (Hermes Ulises et al., 2022; Pita-Díaz & Ortega-Gaucin, 2020). Irrigated crops, including wheat and barley, grown in the dry season (December to May) confront amplified risks from declining water resources and escalating temperatures, leading to adverse impacts on their yields (Klein Tank et al., 2002). Rainfed agriculture, which constitutes a significant portion of the region’s production, is vulnerable to erratic rainfall and prolonged droughts. Maize and sorghum, typically grown between May and December, are largely rainfed and highly dependent on seasonal precipitation. High temperatures during critical growth stages, coupled with drought or waterlogging, can disrupt physiological processes, reduce pollination, and lead to substantial yield losses (Khanzada et al., 2025; Qi et al., 2025; Qiao et al., 2023; Yang et al., 2024; Y. Zhang et al., 2025). Given the agricultural significance and climatic vulnerability of the Bajío, a comprehensive understanding of regional climate change is essential for developing effective adaptation strategies.

Despite the known risks, detailed regional analyses of long-term trends and shifts in climate extremes for the Bajío remain limited. Existing studies have focused on national assessments or utilized coarse spatial data, leaving crucial gaps in understanding localized climate dynamics (Borja-Vega & De La Fuente, 2013; Flores Casamayor et al., 2021). There is a pressing need to address this knowledge gap by employing robust, high-resolution climate datasets and standardized metrics of extremes. Such an understanding is vital for developing location-specific coping strategies, informing agroadvisories for planting calendars, crop variety selection, irrigation scheduling, and risk management for both smallholder and commercial farmers (Ramirez-Villegas et al., 2012). Furthermore, identifying areas with increasing climatic exposure can help prioritize investments in resilient infrastructure and policy reforms, which is especially urgent in a region where climate-induced disruptions can have cascading effects across food systems and rural livelihoods.

Realizing these needs, this research aims to investigate historical precipitation and air temperature trends in the Bajío region of Mexico. Leveraging high-resolution North American reanalysis data, we aim to quantify trends and shifts in extreme climate events and explore the key large-scale global climatic drivers influencing their variability. The results will provide actionable insights into the impact of these extremes on agriculture in the region, ultimately guiding the design of targeted adaptation strategies to mitigate climate risks.

Data and methodology

The study region

The topography of the study area in Bajío, Mexico, is characterized by a dominant midland region, from which the region derives its name, representing 62% of the area, with altitudes ranging between 1600 and 2200 m above sea level (Fig. 1). This midland zone is indicated by the high probability concentration around 1870 m above sea level. A significant portion, 16%, is highland, exceeding 2200 m, culminating in elevations reaching over 4000 m. In contrast, the lowland areas below 1600 m constitute a smaller fraction (22%) of the study area. The region exhibits a semi-arid climate, with precipitation ranging from 300 mm in the northeast to over 1750 mm in the southwest. Approximately 80% of the annual rainfall occurs during the summer months (June to September), while less than 6% falls in winter (December to February).

Fig. 1.

Fig. 1

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The study area showing topography of Bajío region

Climate extremes indices

To analyze climate extremes within the study region, a selection of 22 temperature and precipitation indices was employed, drawing from the 27 indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI) (http://etccdi.pacificclimate.org/). The ETCCDI indices (X. Zhang et al., 2011) are designed to analyze climate trends during the latter half of the twentieth century. The rationale behind utilizing these indices is their standardized approach, enabling consistent and comparable analyses of climate extremes across diverse regions. Specifically, the selected indices allow for the examination of shifts in temperature and precipitation patterns, including changes in the frequency and intensity of extreme events. This methodology facilitates the identification of significant trends and variations in the study area’s climate, providing valuable insights into the region’s vulnerability to climate change impacts.

As detailed in Table 1, the chosen indices encompass a range of metrics, including absolute indices (e.g., maximum and minimum temperatures, maximum, total and intensity of precipitation), threshold indices (e.g., summer days, tropical nights, heavy precipitation days), percentile-based threshold indices (e.g., warm and cold nights/days, and wet days), and duration indices (e.g., warm and cold spell duration, consecutive dry/wet days). These indices, derived from daily maximum (TX) and minimum (TN) temperature and precipitation (PR) data, were selected to capture a comprehensive understanding of extreme climate events. The percentile-based threshold indices were calculated using a consistent reference period of 1981–2022, ensuring comparability across the dataset.

Table 1.

List of selected temperature and precipitation-based extremes indices

Label Index name Index definition Units
1 TN10p Cold nights Percentage of days when TN < 10th percentile %
2 TX10p Cold days Percentage of days when TX < 10th percentile %
3 TN90p Warm nights Percentage of days when TN > 90th percentile %
4 TX90p Warm days Percentage of days when TX > 90th percentile %
5 WSDI Warm spell duration Annual count of days with at least 6 consecutive days when TX > 90th percentile Days
6 CSDI Cold spell duration Annual count of days with at least 6 consecutive days when TN < 10th percentile Days
7 TXx Max TX Annual maximum value of daily maximum temperature °C
8 TXn Min TX Annual minimum value of daily maximum temperature °C
9 TNx Max TN Annual maximum value of daily minimum temperature °C
10 TNn Min TN Annual minimum value of daily minimum temperature °C
11 SU Summer days Annual count of days when TX (daily maximum temperature) > 25 °C Days
12 TR Tropical nights Annual count of days when TN (daily minimum temperature) > 20 °C Days
13 Rx1day Max 1-day precipitation Annual maximum 1-day precipitation mm
14 Rx5day Max 5-day precipitation Annual maximum consecutive 5-day precipitation mm
15 SDII Simple daily intensity The ratio of annual total precipitation to the number of wet days (*RR ≥ 1 mm) mm/day
16 R10 mm Heavy precipitation days Annual count of days when RR ≥ 10 mm Days
17 R20 mm Very heavy precipitation days Annual count of days when RR ≥ 20 mm Days
18 CDD Consecutive dry days Maximum number of consecutive days with RR < 1 mm Days
19 CWD Consecutive wet days Maximum number of consecutive days with RR ≥ 1 mm Days
20 R95p Very wet days Annual total precipitation when RR > 95th percentile mm
21 R99p Extremely wet days Annual total precipitation when RR > 99th percentile mm
22 PRCPTOT Total wet-day precipitation Annual total precipitation in wet days mm
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Further methodological details and a comprehensive overview of the ETCCDI indices can be found at Zhang et al. (2011). *RR, daily precipitation

Climate reference dataset

The study utilized the DAYMET (Daymet) dataset, a high-resolution North American reanalysis product, to obtain daily observed meteorological data (Thornton et al., 2022). Supported by the National Aeronautics and Space Administration (NASA), DAYMET offers a 1-km spatial resolution, providing detailed weather parameters from 1980 to 2022 for a broad geographical area encompassing Canada, Mexico, and the USA. This high spatial resolution is crucial for accurately representing local climate variations, especially in regions with complex topography. The dataset’s temporal coverage allows for a comprehensive analysis of long-term climate trends and extreme events.

DAYMET’s construction relies on a sophisticated methodology that integrates a digital elevation model (DEM) with in situ weather observations from the Global Historical Climatology Network (Thornton et al., 2022). Specifically, it interpolates and extrapolates daily maximum temperature, minimum temperature, and precipitation data, leveraging the DEM to account for elevation-driven climate differences. This approach ensures that the resulting dataset accurately reflects the spatial heterogeneity of weather patterns across North America. The rigorous methodology and high spatial resolution of DAYMET make it a valuable resource for climate studies, particularly those focused on regional-scale analyses.

Trend detection

To analyze trends in extreme climate indices, a robust methodology employing the non-parametric Mann-Kendall (MK) test and Sen’s slope estimator was implemented (Kendall, 1975; Mann, 1945). The MK test, known for its ability to identify statistically significant trends in time series data, was utilized to determine the presence and direction of trends at a 95% confidence level (p < 0.05). Complementing this, Sen’s slope estimator, a method less sensitive to outliers, was applied to quantify the magnitude of detected trends (Sen, 1968). A modified non-parametric MK trend test was applied to account for autocorrelation (Hamed & Ramachandra Rao, 1998). This combined approach offers a reliable alternative to linear regression, particularly for climate data which often exhibits non-normal distributions and outliers (Peterson et al., 2001; Yue et al., 2002). The selection of these methods was further justified by their widespread application in climate studies, as evidenced by their use in analyzing climate indices derived from daily climate data (Cohen et al., 2014; Rettie et al., 2023; Sa’adi et al., 2023).

Composite analysis

To assess the influence of large-scale climate drivers on observed trends in extreme climate indices, we employed a composite analysis approach (Avila-Diaz et al., 2021). This technique enables the examination of how temperature and precipitation extremes respond to distinct phases of major climate oscillations. We focused on six key climate drivers known to influence regional climate variability: the North Atlantic Oscillation (NAO), Arctic Oscillation (AO), Atlantic Multidecadal Oscillation (AMO), Pacific Decadal Oscillation (PDO), Niño 3.4 sea surface temperature index, and the Southern Oscillation Index (SOI). These indices are publicly available and offer standardized representations that facilitate the investigation of their potential influence on climate variability in the study region.

Data acquisition

Monthly index values for each climate driver were obtained from the NOAA Physical Sciences Laboratory (PSL, NOAA Physical Sciences Laboratory), which provides standardized and quality-controlled datasets. The analysis period spanned 1982–2021 to ensure consistency with available climate extremes data.

Composite year selection

Composite years were identified to isolate periods of significant climate anomalies associated with each global index (Table 2). For all indices except Niño 3.4, months with index values exceeding ± 1 standard deviation from their long-term mean (1982–2021) were selected, representing strong positive and negative phases. For Niño 3.4, El Niño and La Niña conditions were defined using thresholds of + 0.5 and −0.5, respectively. This selection strategy ensures that only pronounced anomalies are considered, enhancing the robustness of the analysis linking climate drivers to extreme climate indices.

Table 2.

Years selected for positive and negative anomalies of global climate drivers. Corresponding plots illustrating the temporal patterns of annual anomalies are provided in the supplementary file (Figure S1)

Global indices Years with negative anomalies Years with positive anomalies
SOI 1982, 1983, 1987, 1992, 1993, 1994, 1997, 2015 1988, 1989, 1996, 1999, 2000, 2008, 2010, 2011, 2021
NAO 1980, 1998, 2010, 2012 1982, 1986, 1989, 1990, 1992, 1994, 2015, 2018
AO 1980, 1981, 1985, 1987, 1996, 2010 1989, 1990, 1992, 1994, 2011, 2015, 2020
NINO3.4 1984, 1985, 1988, 1989, 1996, 1999, 2000, 2008, 2011, 2021 1982, 1987, 1992, 1997, 2002, 2015, 2019
PDO 1999, 2000, 2001, 2008, 2009, 2010, 2011, 2012, 2013, 2020, 2021 1983, 1986, 1987, 1993, 1997
AMO 1982, 1984, 1985, 1986, 1992, 1993, 1994 1998, 2003, 2005, 2006, 2010, 2016, 2017, 2020, 2021
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Linking climate drivers to extremes

For each phase of the selected climate drivers, we calculated the mean values of extreme climate indices (e.g., TXx, TNn, RX1day, CDD) across the study region during the identified composite years. These means represent the average conditions during positive and negative phases of each index. We then compared the results between the two phases to assess the magnitude and direction of the influence. This approach enabled us to isolate the impact of each climate driver on temperature and precipitation extremes. The composite analysis thus serves as a diagnostic tool to explore the relationship between large-scale climate variability and local climate extremes, offering a basis for understanding mechanisms that may drive observed trends.

By compositing climate extreme indices for these selected years, the study aimed to reveal the distinct patterns and magnitudes of climate responses associated with each large-scale climate driver.

Results

The analysis of temperature and precipitation reveals a variety of changes in extreme values during the last 40 years in the study area, Bajío region of Mexico. Although this is true for both climate elements, changes in temperature have a much higher degree of spatial coherence. This comes as no surprise since precipitation in the region has more variability than temperature.

Analysis of the annual mean temperature changes (Fig. 2, see Figure S2 for the respective temporal trends) reveals a consistent and unambiguous warming trend, with both mean maximum (TX) and mean minimum (TN) temperatures, as well as their average (TM), demonstrating a clear increase across the study region. Notably, trend analysis of annual mean TN, TX, and TM indicates statistically significant trends covering substantial portions of the region, specifically exceeding 25%, 68%, and 65%, respectively. The magnitude of these annual trends ranges from 0.04 to 0.12 °C per year, highlighting a notable rate of temperature rise. Furthermore, the observed warming is more pronounced for TX and TM compared to TN, suggesting a potential shift in the diurnal temperature range and a disproportionate impact on daytime temperatures.

Fig. 2.

Fig. 2

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The top panels illustrate the spatial distribution of annual trends (°C/year) for minimum (TN), maximum (TX), and average temperatures (TM) in the Bajio region for the period of 1981–2022. Patches highlight regions with statistically significant trends (p < 0.05). The corresponding histograms in the bottom panels depict the frequency distribution of these trends across all grid cells

Trends in temperature indices

The analysis of the 12 temperature-based indices revealed significant changes in both warm and cold extremes over the study period. The findings highlight a clear trend towards warming, with notable spatial variability across the region. Figure 3 illustrates the fraction of pixel cells in the Bajío region exhibiting significant decreasing, non-significant decreasing, non-significant increasing, significant increasing, and no trends. In contrast, Fig. 4 displays the spatial variability of annual (1981–2022) trend estimates with the respective temporal trend depicted in the supplementary file (Figure S3).

Fig. 3.

Fig. 3

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Percentage of pixel cells across in Bajío region showing the different categories of trends in temperature-related extreme indices. -veSign, significant decreasing trends; -ve, non-significant decreasing trends; +ve, non-significant increasing trends; -veSign, significant increasing trends. Significant (p < 0.05, MK test)

Fig. 4.

Fig. 4

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Spatial distribution of annual (1981–2022) trends (Sen’s slope) in temperature-related extreme indices. The areas under patches (depicted as signs) show significant (p < 0.05, MK test) trends

The most prominent finding is the increase in warm temperature extremes across the Bajío region. Considering all positive trends (both significant and non-significant), warm days (TX90p), representing the percentage of days exceeding the 90th percentile of maximum temperature, showed an increase in 92% of the region. Of this, 68% exhibited significant positive trends, with trend magnitudes ranging from 0.5 to 2.0% per year. Similarly, warm nights (TN90p), indicating the percentage of nights exceeding the 90th percentile of minimum temperature, showed an increase in 87% of the region, with 20% exhibiting significant positive trends (trends between 0.5 and 1.5% per year). The warm spell duration index (WSDI), quantifying the length of consecutive warm days, further confirms this warming trend, showing an increase in 71% of the region. Notably, 55% of the region exhibited significant positive trends for WSDI, with trend magnitudes between 0.5 and 2.5 days per year. This suggests that not only are warm days and nights becoming more frequent, but also that warm spells are becoming longer.

Furthermore, summer days (SU), defined as the number of days with a maximum temperature exceeding 25 °C, showed an increase in 92% of the region, with a significant increase in 61% (trends ranging from 2 to 3.5 days per year). This substantial increase in summer days strongly indicates a prolonged and intensified warm season. The maximum daily maximum temperature (TXx) also exhibited an increase in 74% of the region, with significant positive trends in 35% (trends between 0.05 and 0.15 °C per year). This signals that the absolute highest temperatures recorded are also increasing, exacerbating heat stress and related impacts. Finally, the maximum daily minimum temperature (TNx) showed an increase in 73% of the region, with significant positive trends in 26% (trends between 0.05 and 0.1 °C per year), indicating a warming of the warmest nights.

While warming trends dominate, the analysis also examined cold temperature extremes. Considering all negative trends, cold days (TX10p), the percentage of days below the 10th percentile of maximum temperature showed a decrease in 89% of the region. Of this, 60% exhibited significant negative trends, with trend magnitudes ranging from 0.0 to 0.5% per year. Similarly, cold nights (TN10p) showed a decrease in 85% of the region, with 36% exhibiting significant negative trends (trend magnitudes ranging from 0.0 to 0.5% per year). The overall warming is also further confirmed with the cold spell duration index (CSDI), representing the length of consecutive cold days, which showed a decrease in 5% of the region and significant positive trends in only 1%, indicating a very limited change in the frequency of cold spells.

Minimum daily maximum temperature (TXn) showed an overall increase in 64% of the region and a decrease in 36%. Of these, 6% exhibited significant positive trends, while no significant negative trends were observed. Minimum daily minimum temperature (TNn) showed an increase in 66% of the region and a decrease in 34%. Of these, 10% exhibited significant positive trends, while 6% showed significant negative trends (Fig. 3 and Figure S4). Tropical nights (TR) showed an overall increase in 24% and a decrease in 25% in the region, with 52% exhibiting no significant trends (Fig. 3 and Figure S4). This suggests that while overall warming is evident, the absolute minimum temperatures are consistently changing across the Bajío region, and the occurrence of tropical nights is not consistently changing across the region.

Trends in precipitation indices

The analysis focused on eleven precipitation-based indices, revealing a complex picture of changing precipitation patterns. While some indices showed significant trends, others indicated limited change, highlighting spatial and temporal variability across the region. Figure 5 illustrates the fraction of pixel cells in the Bajío region exhibiting significant decreasing, non-significant decreasing, non-significant increasing, significant increasing, and no trends. In contrast, Fig. 6 displays the spatial distribution of annual (1981–2022) Sen’s slope estimates with the respective temporal trend depicted in the supplementary file (Figure S5).

Fig. 5.

Fig. 5

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Percentage of pixel cells across in Bajío region showing the different categories of trends in precipitation-related extreme indices. -veSign, significant decreasing trends; -ve, non-significant decreasing trends; +ve, non-significant increasing trends; -veSign, significant increasing trends. Significant (p < 0.05, MK test)

Fig. 6.

Fig. 6

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Spatial distribution of annual (1981–2022) trends (Sen’s slope) in precipitation-related extreme indices. The areas under patches (depicted as signs) show significant (p < 0.05, MK test) trends

Regarding heavy precipitation events, the annual maximum 5-day precipitation (Rx5day) showed decreasing trends in 95% of the region, with 45% of these decreases being significant, but increasing trends were observed in only 5% of the region, none of which were significant. This suggests a predominant pattern of decreasing intense 5-day precipitation events. For the annual maximum 1-day precipitation (Rx1day), decreasing trends were seen in 55% of the region, with 6% being significant. Conversely, increasing trends were observed in 45% of the region, with 7% showing significant increases. This indicates a more mixed response for single-day extreme rainfall.

Indices related to the frequency of heavy precipitation days, R10mm (days with precipitation over 10 mm) and R20mm (days with precipitation over 20 mm), showed limited overall change. For R10mm, decreasing trends were present in 17% of the region (with 6% being significant), while increasing trends were seen in 6% (with 1% being significant). The majority, 77%, showed no trend. For R20mm, there were no decreasing or increasing trends observed, with 100% of the region showing no trend. This suggests that the frequency of these very heavy rainfall events remains largely stable across the region.

Looking at total precipitation from very and extremely wet days, very wet days total precipitation (R95p) showed decreasing trends in 57% of the region, with 10% being significant. Increasing trends were observed in 36% of the region, with 5% being significant. For extremely wet days total precipitation (R99p), increasing trends were noted in 7% of the region, with 5% being significant. The vast majority, 92%, showed no trend, and no decreasing trends were observed (Fig. 5 and Figure S5). Simple daily intensity (SDII), measuring the average precipitation on wet days, showed decreasing trends in 61% of the region, with 13% being significant. Increasing trends were observed in 39% of the region, with 7% being significant.

The analysis of dry and wet spells revealed contrasting trends. Consecutive dry days (CDD), measuring the length of dry spells, showed increasing trends in 82% of the region, with 4% being significant. Decreasing trends were observed in 17% of the region, with no significant decreases. This indicates a prevalent, though mostly non-significant, tendency towards longer dry spells. Conversely, consecutive wet days (CWD) showed decreasing trends in 94% of the region, with a substantial 77% being significant (Fig. 5 and Figure S5). No increasing trends were observed for CWD, suggesting a widespread decrease in the length of consecutive wet periods.

Finally, total wet-day precipitation (PRCPTOT), which indicates the general amount of precipitation falling on wet days, showed decreasing trends in 92% of the region, with 61% being significant. Increasing trends were observed in 8% of the region, with 1% being significant. This widespread decrease in total wet-day precipitation, alongside the decrease in CWD, points to a potential shift towards drier conditions and shorter wet periods in a large part of the region. The varied percentages of significant regions indicate the trends are not uniform across the Bajío region, highlighting spatial heterogeneity in the observed changes in precipitation.

Influence of large-scale climate drivers

Our analysis of climate extremes in the Bajío region reveals distinct responses to the positive anomalies of major global climate drivers: the Southern Oscillation Index (SOI), North Atlantic Oscillation (NAO), Arctic Oscillation (AO), Niño 3.4 sea surface temperature (SST) indices, Pacific Decadal Oscillation (PDO), and Atlantic Multidecadal Oscillation (AMO). Figure 7 and Fig. 8 illustrate how temperature- and precipitation-related extreme indices respond to both positive and negative anomalies of these global indices.

Fig. 7.

Fig. 7

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Responses of temperature related extreme indices—A) TN90p (warm nights), B) TX90p (warm days), C) SU (summer days), D) WSDI (warm spell duration index), F) TN10p (cold nights), and G) TX10p (cold days)—to positive and negative phases of major global climate indices (SOI: Southern Oscillation Index; NAO: North Atlantic Oscillation; AO: Arctic Oscillation; Niño 3.4 SST; PDO: Pacific Decadal Oscillation; AMO: Atlantic Multidecadal Oscillation). The first and second rows of each panel represent the (+) and (–) phases, respectively

Fig. 8.

Fig. 8

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 Responses of precipitation related extreme indices—A) Rx5day (maximum 5 day precipitation), B) CDD (consecutive dry days), C) SDII (simple daily intensity index), and D) PRCPTOT (annual total wet day precipitation)—to positive and negative phases of major global climate indices (SOI: Southern Oscillation Index; NAO: North Atlantic Oscillation; AO: Arctic Oscillation; Niño 3.4 SST; PDO: Pacific Decadal Oscillation; AMO: Atlantic Multidecadal Oscillation), with the first and second rows of each panel representing the (+) and (–) phases, respectively

During the positive anomalies of most global drivers (excluding PDO), a significant number of temperature-related extremes show positive states, particularly the warm extreme indices. This suggests a general warming influence. Specifically, the AMO and Niño 3.4 show strong, opposing responses to a substantial number of temperature-related extremes. Their positive anomalies correspond with a positive state for warm extremes like warm nights (TN90p), warm days (TX90p), warm spell duration (WSDI), and summer days (SU), indicating a warming effect. Conversely, cold nights (TN10p) and cold days (TN90p) show a negative response to positive AMO and Niño 3.4 anomalies. The positive anomaly of the PDO, however, shows a strong negative response for warm nights (TN90p), warm days (TX90p), warm spell duration (WSDI), and summer days (SU) while cold days (TX10p) show a positive response. This behavior suggests that a positive PDO anomaly tends to cool the Bajío region overall, reducing warm extremes and enhancing cold extremes. The SOI exhibits relatively inconsistent behavior. For instance, its positive anomaly tends to correspond with the negative anomaly for TN90p and the positive anomaly for TX10p.

The analysis of precipitation-related extremes reveals varied influences, often differing from temperature-related extremes responses. Unlike temperature-related indices, most precipitation-related indicators consistently show positive states for the positive anomalies of the SOI. Consecutive dry days (CDD) show a negative response to the positive anomalies of the Niño 3.4, PDO, and AMO, suggesting fewer consecutive dry days during these periods. The annual maximum 5-day precipitation (Rx5day) shows a negative response to positive anomalies of NAO, AO, Niño 3.4, and AMO, but a positive response to SOI. For total wet-day precipitation (PRCPTOT), we observed a negative response to positive anomalies of the NAO, AO, SOI, and AMO. Conversely, PRCPTOT showed a positive response to the PDO. The strong negative responses to NAO, AO, and AMO are notable, suggesting that positive anomalies in these global drivers generally lead to a reduction in total precipitation across the Bajío region. Notably, the strong responses of NAO were also observed for wet days total precipitation of R95p and R99p (Figure S8).

Finally, many precipitation-related indices, including the number of wet days (R1mm), heavy precipitation days (R10mm), very heavy precipitation days (R20mm), consecutive wet days (CWD), very wet days total precipitation (R95p), and extremely wet days total precipitation (R99p), show nearly equal responses to most global indices (Figure S8). Similarly, indices related to the daily temperature range, such as maximum daily minimum temperature (TNx) and tropical nights (TR), also exhibit consistent responses across all investigated indices (Figure S7).

Discussion

Temperature trends

This study analyzes temperature extreme indices in the Bajío region of Mexico from 1981 to 2022, using high-resolution North American reanalysis data. The findings show a clear shift towards warmer conditions, characterized by more warm temperature extremes and a decrease in cold extremes across most of the region.

Widespread warming and intensifying warm extremes

The substantial positive trends in indices such as warm days (TX90p), warm nights (TN90p), warm spell duration (WSDI), summer days (SU), maximum daily maximum temperature (TXx), and maximum daily minimum temperature (TNx) collectively underscore a consistent and intensifying warming pattern across the region. Warm days (TX90p) and warm nights (TN90p) show increases affecting 92% and 87% of the region, respectively. A significant portion of these areas (68% for TX90p and 20% for TN90p) has statistically significant increasing trends, with rates from 0.5 to 2.0% per year for TX90p and from 0.5 to 1.5% per year for TN90p. This aligns with global trends of increasing temperatures and heat extremes (Calvin et al., 2023; Perkins-Kirkpatrick & Lewis, 2020). This consistent warming can have severe implications for crop production for regions like Bajío, which rely heavily on rainfed agriculture and are vulnerable to heat stress (Schmitt et al., 2022; Vogel et al., 2019). For example, wheat and barley are highly sensitive to rising temperatures, with increased warm days, nights, and heatwaves during flowering and grain filling stages leading to floret sterility, shortened grain filling, and reduced yield and grain quality (Asseng et al., 2015; Samarah, 2005). Similarly, maize and sorghum, though differing in heat tolerance, both face significant yield losses when exposed to high temperatures during pollination and early grain development, as prolonged heat spells and elevated night temperatures disrupt kernel set, grain weight, and biomass accumulation (Lobell et al., 2011; Prasad et al., 2008).

The substantial rise in summer days and increasing maximum daily maximum temperature further solidify this trend, indicating a prolonged and more intense warm season. Additionally, the significant decrease in cold days (TX10p) observed in 89% of the region (60% significant, 0.0 to 0.5% per year) and cold nights (TN10p) observed in 85% of the region (36% significant, 0.0 to 0.5% per year) further confirms this overall warming. These findings correspond to observed significant decreasing trends in cold days and cold nights in the broader North American region over past decades, potentially driven by shifts in atmospheric circulation (Avila-Diaz et al., 2021; Cohen et al., 2014). The reduction in cold extremes can have both positive and negative effects; it may lower frost damage to some crops, but it could also impact the chilling needs of certain perennial plants and enhance pests and diseases spread, which are often limited by cold temperatures (Penfield et al., 2021; Roussos, 2024).

Changes in minimum temperatures and spatial variability

In terms of other indices, the minimum daily maximum temperature (TXn) showed an overall increase in 64% of the region, with 6% displaying significant positive trends, and no significant negative trends noted. Similarly, the minimum daily minimum temperature (TNn) increased in 66% of the region, with 10% showing significant positive trends and 6% showing significant negative trends. These mixed trends for TXn and TNn suggest a more complex and variable response of extreme minimum temperatures across the Bajío region, indicating that while the area is warming, the coldest days and nights do not consistently increase or decrease everywhere. The occurrence of tropical nights (TR) across a large portion of the region presents varied responses, with an increase of 24% and a decrease of 25%, while 52% showed no significant trends. This suggests that while the overall temperature envelope is shifting towards warmer extremes, the absolute minimum temperatures and the occurrence of tropical nights are not uniformly following this trend. This could be attributed to a variety of factors, including local microclimates, land use changes, or specific atmospheric dynamics that are buffering or counteracting the broader warming trend (Seneviratne et al., 2025; Shen & Zhao, 2021; Zuo et al., 2021).

The observed spatial variability in these trends highlights the complexity of regional climate responses to global warming and emphasizes the importance of localized studies to capture these factors (Giorgi et al., 2022; Tebaldi et al., 2021). This local variability has implications for agriculture, particularly for winter crops like wheat and barley that need cold temperatures for high yields (Fischer et al., 2022). The lack of consistently increasing trends in extreme minimum temperatures across the entire region might indicate that these crops can continue to be grown, though careful monitoring of specific microclimates remains essential.

Implications for the Bajío region

The link between climate extremes and maize yield is evident in North America (Vogel et al., 2019). The pronounced warming trend in the Bajío region raises critical concerns, given its contribution of approximately 25% of national maize production. The increased frequency and intensity of warm extremes can lead to heightened heat stress, increased evapotranspiration, and potential water scarcity, impacting crop yields and water availability (Lesk et al., 2021, 2022). Furthermore, the observed changes can exacerbate existing vulnerabilities, particularly for vulnerable populations and ecosystems. Understanding the spatial and temporal variability of these trends is crucial for developing effective adaptation strategies and mitigating the impacts of climate change in the region.

Precipitation trends

The analysis of precipitation trends in the Bajío region reveals a complex and spatially variable picture, contrasting with the more coherent warming trends observed in temperature (Rettie et al., 2023; Sa’adi et al., 2023). Our findings indicate a predominant pattern of decreasing intense 5-day precipitation events (Rx5day), affecting 95% of the Bajío region. This contrasts with previous evidence from global in situ data analysis that shows an increase in the frequency of extreme precipitation across most regions of the Northern Hemisphere extratropic during the last half-century (Frich et al., 2002). The annual maximum 1-day precipitation (Rx1day) exhibited a more mixed response, with decreasing trends in 55% of the region and increasing trends in 45% of the region. This divergence suggests the overall intensity of extreme precipitation might be changing in a complex manner, potentially with less frequent but not necessarily less intense single-day events. Previous comprehensive analyses utilizing high-quality station data have reported significant global trends in both Rx1day and Rx5day (Sun et al., 2021) underscoring the importance of regional-scale studies to understand local drivers.

The general absence of significant trends in indices related to the frequency of heavy precipitation days (R10mm, R20mm) and very wet/extremely wet days (R95p, R99p) suggests the frequency of these events remains relatively stable. This is a crucial distinction from the intensity and duration aspects. Similarly, the widespread decrease in consecutive wet days (CWD) and total wet-day precipitation (PRCPTOT) points to a significant shift towards shorter, less frequent wet periods. This contrasts with the observation that despite warming, consecutive dry days (CDD) did not show consistent significant increases, which aligns with findings from a study in the nearby state of Zacatecas (Pita-Díaz & Ortega-Gaucin, 2020). The combined effect of these trends suggests a potential redistribution of rainfall, with overall reduced wetness and shorter wet periods.

Implications and future considerations

The observed spatial heterogeneity in precipitation trends across the Bajío region underscores the complexity of precipitation-related changes and highlights the significant influence of local factors on regional precipitation patterns (Pita-Díaz & Ortega-Gaucin, 2020; Rettie et al., 2023; Ziegler et al., 2024). This heterogeneity in most of the precipitation-related extreme trends could also be related to uncertainties inherent in the gridded data used for analysis (Diaconescu et al., 2018).

Despite the complex nature of precipitation-related extreme trends, the statistically significant connections between extreme precipitation and temperature demand critical attention (Sun et al., 2021). The observed precipitation changes, while spatially variable, would have significant implications for water resource management, agriculture, and ecosystem health in the region (De Lima et al., 2023). The observed decreased frequency and duration of wet periods, along with the overall decrease in total wet-day precipitation, will intensify water scarcity and pose challenges for rainfed agriculture. Conversely, the lack of significant changes in dry spell duration may mitigate some of the most severe impacts of warming on water availability, but the overall shift towards drier conditions necessitates careful planning and adaptation. This is crucial for ensuring sustainable water management as the climate changes, especially given the projected widespread decrease in seasonal precipitation during Mexico’s main cropping season (Nazarian et al., 2024).

Composite analysis

The composite analysis highlights the complex influence of global climate drivers on regional climate extremes within the Bajío region. The observed differential responses of temperature and precipitation extremes to major global climate drivers in the Bajío region underscore the complexity of regional climate variability. While the positive anomalies of the Atlantic Multidecadal Oscillation (AMO) and Niño 3.4 consistently drive a warming influence, manifesting as increases in warm extremes and decreases in cold extremes, the Pacific Decadal Oscillation (PDO) stands out with an opposing effect. A positive PDO anomaly promotes regional cooling by reducing warm extremes and enhancing cold extremes. This dichotomy highlights the unique teleconnections and regional sensitivity to distinct oceanic and atmospheric forcing mechanisms, suggesting that the Bajío region’s climate is shaped by a confluence of large-scale oceanic and atmospheric patterns with contrasting local impacts.

Furthermore, the analysis reveals that different climate drivers exert distinct and sometimes opposing influences on various aspects of climate extremes in the Bajío region. The more consistent positive states of precipitation indices during the positive anomalies of the Southern Oscillation Index (SOI), contrasting with the varied and sometimes negative responses to other drivers like the North Atlantic Oscillation (NAO), Arctic Oscillation (AO), Niño 3.4, and AMO for specific precipitation metrics (e.g., annual maximum 5-day precipitation, Rx5day), exemplify this. Notably, the strong influence of the NAO, AO, and AMO on total wet-day precipitation (PRCPTOT) suggests that these global drivers could serve as valuable indicators for seasonal forecast advisories, potentially aiding in more accurate precipitation predictions for the region. Larger scale analysis in the North America region showed that large-scale global drivers such as NAO and AO distinctly influence extreme climate indices (Avila-Diaz et al., 2021). This intricate interplay calls for a complex understanding of each driver’s specific teleconnection pattern and its implications for both temperature and precipitation regimes in the Bajío region, emphasizing the need for comprehensive regional analysis that accounts for these complex interactions (Molina & Allen, 2020; Tysa et al., 2025). Further research is imperative to disentangle the relative contributions of global and local factors in driving climate variability in the Bajío region and to refine our understanding of the complex interactions between climate drivers and regional climate extremes.

Conclusion and recommendations

Our comprehensive analysis of temperature and precipitation extremes in the Bajío region over the last 40 years unveils critical insights into the region’s changing climate. A dominant and consistent warming trend is clear, marked by a significant increase in various warm temperature extremes and a corresponding decrease in cold extremes. This warming poses substantial challenges for agricultural sustainability, raising concerns about heat stress on crops and increased water demand. Conversely, precipitation patterns present a more complex and spatially heterogeneous narrative. We observed a predominant decrease in intense 5-day precipitation events, the duration of consecutive wet days, and total wet-day precipitation, contrasting with more mixed or stable trends in single-day extremes and the frequency of heavy rainfall events. These complex changes in precipitation have direct implications for water availability and agricultural practices.

The strength of this study lies in its novel, regional focus, employing high spatial resolution data which is crucial for capturing localized climate impacts, and our exploration of a large set of global drivers, offering a more complete understanding of large-scale influences. This foundational research will undoubtedly aid future studies in the region. However, it is important to acknowledge certain limitations. The composite analysis was constrained by unbalanced data during the selection of years for global index anomalies. Furthermore, the use of reanalysis data, while offering extensive spatial coverage, may sometimes mask out finer-scale extreme events.

To build upon these findings, we offer several recommendations for future research and policy development. Future studies should be supported by complementary analyses using weather station data to gain a deeper understanding of local factors influencing these trends. Additionally, a more detailed analysis including seasonal and sub-seasonal time scales, particularly relevant to agricultural practices, would provide invaluable insights. From a policy perspective, the observed warming necessitates the development of robust adaptation strategies to manage heat stress and water scarcity in agriculture. Similarly, the complex and predominantly decreasing precipitation trends underscore the need for effective water resource management policies tailored to potential shifts towards drier conditions and shorter wet periods. These efforts are vital for ensuring the continued agricultural sustainability and resilience of the Bajío region in the face of ongoing climate change.

Supplementary Information

Below is the link to the electronic supplementary material.

ESM 1 (3.5MB, docx)

(DOCX 3.47 MB)

Acknowledgements

We appreciate Kai Sonder, GIS Laboratory Manager at CIMMYT, Mexico, for contributing the Bajío region shapefile and for their helpful direction to online resources.

Author Contribution

FMR, SGF, and TBS: Conceptualization. FMR: methodology, formal analysis, visualization, soft-ware and writing—original draft preparation. SGF and TBS: Project administration and funding acquisition, supervision, investigation and resources. FMR, MWG, SGF, TSS, and TBS: writing–review and editing. All authors reviewed and contributed to the final version of the manuscript.

Funding

This research was conducted as part of the project “Regenerative Agriculture for Climate Resilient Farms and Value Chains,” a collaborative effort jointly supported by the Foundation for Food & Agriculture Research (FFAR) and PepsiCo under the AgMission Initiative. Additional analytical support was provided through the CGIAR Science Program on Climate Action.

Data Availability

No datasets were generated or analysed during the current study.

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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Supplementary Materials

ESM 1 (3.5MB, docx)

(DOCX 3.47 MB)

Data Availability Statement

No datasets were generated or analysed during the current study.

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