Towards a new climate regime: heatwaves proliferate and reshape seasonality in the world's largest tropical wetland

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The simultaneous occurrence of droughts and heatwaves intensifies vegetation flammability and has been a determining factor in the amplification of fires. Given this scenario, this study aimed to quantify and characterize future changes in the frequency, duration, and seasonality of heatwaves in the Pantanal for the period 2030–2060, analyzing projections under the SSP1-1.9, SSP2-4.5, and SSP5-8.5 emission scenarios, compared to the climatological normal of 1991–2020. For this purpose, four CMIP6 models were validated against the ERA5 reanalysis, with the MRI-ESM2-0 model selected for its superior performance in representing the daily maximum temperature in the biome area. The results show a significant increase in the occurrence of these events in all scenarios, with an average increase of + 126% to + 181% in the number of days above the extreme heat threshold. Heatwaves become more prolonged and concentrated in spring and autumn, indicating a restructuring of the biome's thermal seasonality. These findings suggest that the Pantanal is moving towards a new climate regime, characterized by intensified thermal stress and the expansion of the “hot and dry season,” which will have direct implications for biodiversity and fire management. WSDI HWFI CAMS-CSM1-0 EC-Earth3-Veg GFDL-ESM4 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Introduction Anthropogenic global warming has increased the frequency, duration, and spatial extent of extreme climate events worldwide, including the simultaneous occurrence of heatwaves and droughts. When these extremes overlap, they create compound events whose synergistic effects significantly elevate wildfire risk and degrade air quality on regional and global scales (SANTOS et al., 2024). Luo et al. (2022) demonstrated that spatiotemporally contiguous heatwaves have risen markedly in frequency, magnitude, duration, and spatial extent across China since the 1960s, primarily due to intensified global and regional warming and the influence of large-scale atmospheric circulation patterns, such as Rossby waves and atmospheric blocking. Similarly, Zhang et al. (2025) emphasize that heatwaves often interact with other extreme phenomena, such as marine and coastal heat anomalies, resulting in amplified socio-environmental impacts. These findings reinforce the understanding that compound extremes, whether involving hydrological or thermal stress (such as compound drought–heatwave events, or CDHW), are becoming more frequent and complex under anthropogenic warming, with cascading consequences for ecosystems and human health. In the Pantanal, these processes have become increasingly evident, as prolonged dry spells coupled with record-breaking heat intensify fire activity and the persistence of atmospheric pollutants.Understanding how heatwaves reshape thermal and hydrological regimes is essential not only for climate diagnostics but also for anticipating ecological and socio-environmental transformations. The Pantanal, as a keystone tropical wetland, provides an exemplary case of regional-scale climate reorganization under global warming. Although South America has been identified as a hotspot for compound extreme events, their spatial patterns and impacts remain relatively under-documented in the scientific literature (Santos et al., 2024; Feron et al., 2024). Recent studies, however, highlight an intensification of these phenomena across southern South America. Trends observed in southeastern Brazil, where the frequency, intensity, and duration of hot and dry events have increased markedly (Perkins-Kirkpatrick & Lewis, 2020; Cunha et al., 2019), are highly relevant for the La Plata Basin, which encompasses the Pantanal. Within this critical basin, warm and dry conditions have become more frequent (Barrucand et al., 2014; Tencer et al., 2016), and recent evidence indicates a rise in compound precipitation–temperature extremes (Olmo et al., 2020; Hao et al., 2018). In this context of increasing climate vulnerability, the Pantanal, the world’s largest continuous floodplain, stands out. Its structure and functioning are governed by a seasonal flood pulse, which generates a complex mosaic of aquatic and terrestrial habitats and supports remarkable biodiversity (ALHO; SABINO, 2012; TOMAS et al., 2019). Ecologically, the Pantanal is a fire-dependent biome, in which fire plays a crucial role in maintaining ecosystem structure and processes (PIVELLO et al., 2021). However, the biome faces severe threats, notably the conversion of native vegetation to pastures and croplands, particularly on adjacent plateaus where the headwaters of its rivers are located (ALHO; SABINO, 2012; MARENGO et al., 2021). Furthermore, the Pantanal’s sensitivity to abrupt atmospheric disturbances is evident not only through heat extremes but also through cold snaps, which historically have caused significant socio-economic and environmental impacts, including livestock mortality and increased incidence of respiratory diseases (RAMOS et al., 2025). Drought conditions render vegetation highly flammable, while heatwaves, with extreme temperatures and low humidity, elevate flammability to critical levels (LIBONATI et al., 2022). The 2020 fire season, which consumed approximately one-third of the biome (PLETSCH et al., 2021), exemplifies the extreme consequences of these combined factors. SHIMABUKURO et al. (2023) estimated that 44,998 km² burned in the Brazilian portion, resulting from the concurrence of drought and persistent thermal anomalies (LIBONATI et al., 2020; MARENGO et al., 2021; PIVELLO et al., 2021). Studies indicate that the co-occurrence of these compound drought–heatwave events (CDHW) accounted for more than 70% of the affected area (LIBONATI et al., 2022; SANTOS et al., 2024). This process led to the release of over 115 million tons of CO₂ and the estimated death of 17 million vertebrates, including endemic and threatened species (TOMAS et al., 2021; SHIMABUKURO et al., 2023). Overall, heatwaves emerge as a primary driver of wildfire intensification in the Pantanal. Despite advances in understanding these dynamics, substantial knowledge gaps remain, particularly regarding the quantification of future changes in extreme temperature regimes and their implications for environmental management and planning (LIBONATI et al., 2020; PIVELLO et al., 2021). Given the biome’s intrinsic vulnerability, where extreme heat already acts as a trigger for ecological disasters, the central hypothesis of this study is that global warming is inducing a fundamental restructuring of the Pantanal’s climate regime. It is hypothesized that heatwaves, as defined by historical climatology (1991–2020), will shift from sporadic events to a chronic and dominant seasonal feature in the near future (2030–2060). To test this hypothesis and assess the magnitude of this transformation, the main objective of this study is to quantify and characterize changes in the frequency, duration, and seasonality of heatwaves in the Pantanal under a near-future scenario (2030–2060), across different socioeconomic development pathways (SSPs), using a fixed climatological threshold from the historical period (1991–2020) as reference. Materials and methods Spatial and temporal delimitation The temporal analysis was based on two 30-year climatological periods. The historical reference period was defined as 1991–2020, in accordance with the latest standard climatological normal established by the World Meteorological Organization (WMO). Adhering to this international guideline ensures that the results are methodologically comparable with climate studies and reports at a global scale. For projections, the period 2030–2060 was selected, maintaining the same 30-year duration to ensure a statistically balanced and consistent comparison between past climate conditions and those expected in the near future. The spatial domain of the study is the Pantanal biome (Fig. 1 ). Its delineation was performed in two steps: first, for data acquisition via Application Programming Interface (API), a broad rectangular area was defined (14°S to 23°S, 60°W to 53°W). Subsequently, for analyses, the final spatial subset was adjusted to the official biome boundaries using its vector polygon (shapefile), with an external one-pixel buffer applied to mitigate edge effects on the results. Data sources and climate scenarios Daily maximum temperature (tasmax) data were obtained from two main sources: Historical Period (1991–2020): ERA5 reanalysis dataset (reanalysis-era5-single-levels), provided by the European Centre for Medium-Range Weather Forecasts (ECMWF). Future Period (2030–2060): Climate projections were derived from Earth System Models (ESMs) participating in the sixth phase of the Coupled Model Intercomparison Project (CMIP6). For validation, four distinct models were considered: CAMS-CSM1-0 (China), EC-Earth3-Veg (Europe), MRI-ESM2-0 (Japan), and GFDL-ESM4 (USA). Projections were analyzed under three Shared Socioeconomic Pathways (SSPs), which describe alternative plausible socioeconomic futures and the resulting greenhouse gas emissions trajectories: SSP1-1.9: Represents a sustainability and low-emission scenario. This optimistic pathway is characterized by global development prioritizing well-being and equity, strong climate mitigation, rapid transition to renewable energy, and international cooperation, aligning with the goal of limiting global warming to 1.5°C above pre-industrial levels (O’Neill et al., 2016). SSP2-4.5: Known as the intermediate or “middle-of-the-road” scenario. It describes a world following historical development patterns, with uneven progress and moderate climate policies. GHG emissions continue to rise in the near term, stabilize mid-century, and gradually decline thereafter, resulting in a radiative forcing of 4.5 W/m² by 2100 (O’Neill et al., 2016). SSP5-8.5: Corresponds to a high-emission scenario driven by fossil-fuel-intensive development. This pathway describes rapid global economic growth, fossil-based energy exploitation, and a high-consumption lifestyle, resulting in continuously increasing emissions and the most extreme warming scenario (radiative forcing of 8.5 W/m² by 2100) (O’Neill et al., 2016). Data preprocessing and harmonization NetCDF data underwent preprocessing to ensure consistency and comparability: Unit and Calendar Conversion: Temperatures were converted from Kelvin (K) to Celsius (°C). All time series were standardized to a non-leap-year Gregorian calendar (365 days/year). Spatial Harmonization: To ensure spatial comparability, MRI-ESM2-0 model data were regridded to the native ERA5 grid (0.25° × 0.25°) using a first-order conservative remapping method. This step guarantees that subsequent analyses are performed on an identical spatial grid. Climate model validation and selection To assess CMIP6 model performance in reproducing daily maximum temperature (tasmax) over the Pantanal, model outputs were compared against ERA5 reanalysis. The validation period was 2015–2024, representing the most recent temporal overlap between observed reanalysis and the start of historical model simulations, allowing a direct evaluation of how well models replicate observed climate. Performance was quantified using multiple statistical metrics, each evaluating a distinct aspect of model skill: Mean Bias Error (MBE): Identifies systematic model bias, indicating whether the model overestimates (positive bias) or underestimates (negative bias) observed temperatures. Root Mean Square Error (RMSE): Measures the average magnitude of simulation errors, providing an overall measure of model accuracy. Unlike MBE, RMSE penalizes larger errors and does not indicate error direction. Pearson Correlation Coefficient (r): Quantifies the strength and direction of the linear relationship between simulated and observed values, evaluating how well the model captures variability and timing of temperature events. Kling-Gupta Efficiency (KGE): Provides an integrated diagnostic assessment, decomposing model performance into correlation, bias, and variability components. A KGE value close to 1 indicates near-perfect agreement between simulation and observation. Kolmogorov–Smirnov (KS) Test: Compares cumulative probability distributions of simulated and observed data, assessing whether both samples come from the same distribution and verifying overall similarity in climatic variability beyond mean values. Although multi-model ensembles are widely used to assess uncertainties, several recent studies (Reboita et al., 2024; Costa et al., 2024) have demonstrated that MRI-ESM2-0 consistently ranks among the top performers for South America, providing robust simulations of temperature extremes. Therefore, the use of this single, best-performing model ensures internal physical consistency across scenarios while maintaining computational tractability Heatwave definition and threshold strategy Heatwaves were identified following the methodology of the Expert Team on Climate Change Detection and Indices (ETCCDI). An event was defined as a period of six or more consecutive days during which daily maximum temperature (tasmax) exceeded the 90th percentile (P90), calculated for each calendar day from the reference climatology. Two threshold strategies were employed to ensure a comprehensive analysis: Preliminary Analysis with Moving Threshold: Initially, the P90 was calculated independently for the historical period (1991–2020) and each future scenario (2030–2060). This approach allows the assessment of heat anomalies extreme relative to each period’s internal climate. Main Analysis with Fixed Threshold: For the core analysis, a single P90 threshold was established using historical reference data only. This fixed threshold was then applied to all datasets (historical and future). This approach is essential to quantify absolute changes in frequency and duration of extreme heat days relative to current climate. Indices calculation Using the fixed P90 threshold, two main ETCCDI indices were calculated to characterize heatwaves: Warm Spell Duration Index (WSDI): Total number of days per year included in a heatwave event. Heatwave Events (HWFI): Total number of events (≥ 6 consecutive days) occurring per year. Annual, monthly, and seasonal time series for both indices were generated for the historical period and each future SSP scenario, always using the same reference threshold to ensure direct comparability. Comparative and statistical analysis Comparisons between future and historical climates were performed by calculating absolute differences (Δ) for each index at each grid point: Δ = FutureSSP − Past Positive Δ values indicate increases in heatwave duration or frequency. Difference fields were exported as GeoTIFF files. In addition to spatial analysis, summary statistics, such as the spatial mean of indices over the Pantanal, were computed to obtain aggregated values representing projected biome-wide changes. Software and computational tools NetCDF file operations, including temporal aggregation, spatial subsetting, and index calculations, were conducted using Climate Data Operators (CDO) and NetCDF Operators (NCO). Workflow automation and integration were managed using Python scripts. All cartographic products and spatial visualizations were generated in QGIS (version 3.42). Results Climate model validation and selection 2.2 While multi-model ensembles provide a broader uncertainty envelope, the high skill of MRI-ESM2-0 in reproducing observed heat extremes supports the reliability of the projections presented here. The evaluation of selected CMIP6 models against the ERA5 reanalysis for the Pantanal region revealed clear differences in their ability to represent near-surface daily maximum temperature (Tmax) over the period 2015–2024. Figure 2 summarizes the overall performance statistics (bias and RMSE) for the four models analyzed. Among them, MRI-ESM2-0 showed the best agreement with the ERA5 reference, with a mean bias of + 0.72°C and an RMSE of 1.48°C, substantially lower than those observed for CAMS-CSM1-0 (+ 1.9°C; RMSE = 2.8°C) and EC-Earth3-Veg (− 1.3°C; RMSE = 1.9°C). GFDL-ESM4 also exhibited reasonable performance (bias = − 0.9°C; RMSE = 2.1°C). Temporal correlation analysis confirmed these results: MRI-ESM2-0 achieved the highest correlation coefficient (r = 0.87) and the highest Kling-Gupta Efficiency (KGE = 0.81), indicating excellent consistency in reproducing both the seasonal and interannual variability of Tmax. In contrast, CAMS-CSM1-0 systematically overestimated temperatures during the warm season, while EC-Earth3-Veg and GFDL-ESM4 tended to underestimate them. When evaluating extreme temperature indices (Fig. 3 ), MRI-ESM2-0 again exhibited the best overall performance. The model simulated an annual maximum temperature (TXx) of 41.2°C, in excellent agreement with the ERA5 reference value of 41.5°C. The Warm Spell Duration Index (WSDI), which measures the number of days per year belonging to periods of at least six consecutive days with Tmax above the 90th percentile of the reference series (Alexander et al., 2006), was also well represented. MRI-ESM2-0 simulated an average of 58 days per year, compared to 63 days per year observed in ERA5. The other models showed larger discrepancies, ranging from 47 days/year (CAMS-CSM1-0) to 52 days/year (GFDL-ESM4). The ability of MRI-ESM2-0 to represent heatwave events from 2019 to 2021, which coincided with severe drought and large-scale wildfires in the Pantanal, was particularly remarkable. As illustrated in Fig. 4, the scatter plot between daily Tmax from ERA5 and MRI-ESM2-0 shows a relationship close to the 1:1 line (r ≈ 0.87), confirming that the model adequately reproduces both the magnitude and temporal dynamics of extreme heat days. Overall, these results justify the selection of MRI-ESM2-0 as the most reliable model for subsequent analyses. This choice is further supported by the literature, which highlights the model’s robustness in representing South American climate. The assessment by Reboita et al. (2024), which compared 50 CMIP6 models, identified MRI-ESM2-0 as one of the top-performing models for simulating temperatures in Brazil. Based on these findings, Neto et al. (2026) also selected the same model for a study on extremes in southern Minas Gerais. Coherence and divergence of ssp scenarios A key step in the analysis was to examine the interdependence among future climate trajectories, visualized in the correlation matrix in Fig. 5 . The results reveal divergence patterns consistent with the socioeconomic narratives underlying each pathway. Far from indicating model inconsistency, the correlations confirm the sensitivity of projections to the different forcing conditions imposed by each scenario. Individual coefficient analysis shows a clear hierarchy of association. The deep mitigation scenario, SSP1-1.9, demonstrates near-complete independence from the others. Its correlation with SSP2-4.5 is essentially zero (r = 0.001) and with SSP5-8.5 is weakly positive (r = 0.232), indicating that the adoption of stringent climate policies leads to a fundamentally distinct future trajectory. Conversely, the higher-emission scenarios, SSP2-4.5 and SSP5-8.5, exhibit a moderate positive correlation (r = 0.545). This level of association suggests that, although distinct, these two pathways share greater structural similarity in their temporal variations, reflecting the climate physics of a world with insufficient or absent mitigation policies. Therefore, the matrix illustrates the progressive statistical divergence between the sustainable scenario and higher-warming trajectories, while validating the internal coherence of the model projections across different socioeconomic futures. Heatwaves with a moving threshold The initial analysis, based on a moving threshold, provides a perspective on how heatwave anomalies evolve relative to the mean climate of each period, as summarized in Table 1 . In the high-emission scenario (SSP5-8.5), both the duration (+ 18.26%) and frequency (+ 9.03%) of events increase, indicating a future with more intense heat extremes even relative to an already warmer future climate. However, a counterintuitive result emerges under the intermediate scenario (SSP2-4.5), which shows reductions in both duration (− 30.04%) and frequency (− 23.67%) of heatwaves. This apparent decrease does not indicate a milder future climate; rather, it is a methodological artifact inherent to using a moving threshold. As the background climate warms during 2030–2060, the threshold defining "extreme" conditions (the 90th percentile) also rises. Consequently, it becomes statistically more difficult for days to exceed this new, higher baseline. This effect thus masks the increase in absolute thermal stress to which the biome will be exposed relative to historical conditions. To quantify the real impact of climate change relative to the present climate and overcome this interpretive limitation, a second analysis was conducted using a fixed threshold anchored in the 1991–2020 reference period. Table 1 Heatwave Index (HWFI) in the Pantanal (Moving Threshold). Comparison between the historical period (NC) and future scenarios (2030–2060), showing totals for the 30-year period and annual averages in parentheses. Scenario HWFI -Days Δ vs. Histórical Δ% HWFI -Events Δ vs. Histórical Δ% NC 273 (9/ano) - - 34 (1/ano) - - SSP1-1.9 280 (9/ano) + 7,55 + 2,77% 32 (1/ano) -1,88 -5,51% SSP2-4.5 191 (7/ano) -81,90 -30,04% 26 (1/ano) -8,07 -23,67% SSP5-8.5 323 (11/ano) + 49,77 + 18,26% 38 (2/ano) + 3,08 + 9,03% Recent studies show that when the climatological baseline is updated to a warmer period, the number of days classified as extremes decreases, even as warming continues (Thomas et al., 2023; Li, Zhou & Zhang, 2025). Thomas et al. (2023) demonstrated that in the United States, updating the climatology from 1981–2010 to 1991–2020 led to a marked reduction in the frequency of hot extremes and an apparent increase in cold extremes due to the elevated percentile threshold. Similar results were reported in China by Li, Zhou, and Zhang (2025), where the baseline shift delayed the “statistical emergence” of heat extremes, making them less frequent in the context of ongoing warming. This statistical behavior is consistent with the theory of temperature distribution shifts under global warming (Schär et al., 2004) and is recognized by the IPCC (2021) as an inherent effect of defining percentile-based extreme indices. Thus, even with an increase in mean temperatures, the number of days exceeding the 90th percentile is statistically lower, as the reference threshold shifts upward, requiring stronger anomalies to classify an event as extreme. As one of the main objectives is to establish a direct comparison with the historical climate, the threshold was kept fixed at the 1991–2020 climatological baseline. Accordingly, the analyses in this stage were deliberately concluded here. Heatwaves with a fixed threshold In contrast to the moving threshold analysis, the application of a fixed threshold, anchored in the historical climatology (1991–2020 P90), reveals the true magnitude of future warming. Results show an increase in the number of extreme heat days in the Pantanal across all scenarios (Table 2 ). What is currently considered an extreme heat day (averaging 36 days per year, or ~ 10% of the time) will become much more frequent. In the future, the region will experience between 83 (SSP1-1.9) and 103 (SSP5-8.5) extreme heat days annually. Table 2 Average annual number of days with Tmax ≥ P90 (fixed threshold 1991–2020) and corresponding fraction of the year. Scenario Days ≥ P90 (days/yr) Fraction of Year (%) NC 36,46 9,99 SSP1-1.9 82,60 22,63 SSP2-4.5 85,84 23,52 SSP5-8.5 102,70 28,14 The magnitude of this change is substantial, as quantified in Table 3 . The Pantanal is projected to experience 46–66 additional hot days per year relative to the present climate. In relative terms, this represents an increase of at least 126% (SSP1-1.9), rising to 181% under the most pessimistic scenario (SSP5-8.5). A clear “dose-response” relationship emerges: the higher the emission forcing of the scenario, the more severe and pronounced the increase in the frequency of extreme heat days. Table 3 Absolute and relative changes in the number of days ≥ P90 relative to the historical period (1991–2020). Scenario Absolute Change (days/yr) Absolute Change (days/yr) SSP1-1.9 + 46,14 + 126,6 SSP2-4.5 + 49,38 + 135,4 SSP5-8.5 + 66,24 + 181,8 Beyond counting days ≥ P90, we assessed heatwave occurrence. Figure 6 summarizes the annual change in the number of days within heatwaves (HWFI) and the number of events per year. The average number of heatwave days per year increases from 8.8 (historical) to 48.9 (SSP1-1.9), 50.7 (SSP2-4.5), and 69.6 (SSP5-8.5), while events per year rise from 1.1 to 4.9–6.3. Thus, the future projects both more frequent and longer-lasting events. From a climatological perspective, these results indicate a profound shift in the temperature regime, characterized by a strong expansion of the upper tail of the statistical distribution. Practically, this means that heat events currently considered rare or extreme will become a regular feature of the future climate. The fraction of the year with “very hot” days will increase from ~ 10% to between 23% and 28%, i.e., nearly one-quarter of the year, even under the highest mitigation scenario. This “normalization” of extremes represents the clearest signature of intensified thermal stress that the Pantanal ecosystem will face in the coming decades. This projection is particularly alarming, as the Pantanal is a biome already operating under severe climate stress, with recent extreme events serving as a precursor of what is to come. The catastrophic fires of 2020, which devastated nearly one-third of the biome, were directly linked to a combination of severe drought and unprecedented heatwaves (Libonati et al., 2020). Annual aggregates indicate a general increase in heatwave occurrence, yet evaluation of seasonal distribution is required to understand the restructuring of the thermal regime. Figure 7 illustrates the monthly cycle of heatwave days, allowing comparison between historical patterns and future projections. The graphical representation shows that the historical pattern was characterized by a low incidence of heatwave days, with monthly averages rarely exceeding one day. In contrast, future scenarios indicate a marked concentration of events in the second half of the year. The transition begins in August, which, although historically registering minimal activity, shows a significant increase in hot days, marking the start of the period with highest incidence. The greatest concentration of events occurs during the austral spring. October emerges as the seasonal peak in all scenarios, with the mean number of heatwave days increasing from approximately 1.1 in the reference period to 9.3 (SSP1-1.9), 10.6 (SSP2-4.5), and 11.6 (SSP5-8.5). Although the historical period is used as a comparative reference in the analyses, it should be noted that it does not represent a stable or “natural” climate state. This interval already reflects transformations induced by global warming observed in recent decades, marked by increased frequency of droughts and heatwaves in the Pantanal. Marengo et al. (2021) emphasize that these processes have pushed the biome toward a condition of growing vulnerability, approaching ecological resilience thresholds. Warming acts synergistically, intensifying water deficits, increasing vegetation flammability, and compromising biological cycles. Consequently, even the “historical” climatology should be understood as part of a transition context, in which current extreme conditions already herald the consolidation of a new thermal and hydrological regime in the Pantanal. This transition to a new climate regime becomes even more evident when contrasting projected future heat extremes with the observed dynamics of cold extremes. Recent climatological analyses of frost events in the Pantanal, for 1981–2023, indicate a decreasing trend in both frequency and intensity, which have also become progressively drier over recent decades (Ramos et al., 2025). Therefore, the data point to a clear restructuring of the extremes regime: while cold waves, a historical component of the biome’s climate variability, are declining, heatwaves are projected not only to increase but to become the dominant event. Spatial and seasonal analysis Spatial analysis The spatial distribution of temperature anomalies indicates that the increase in the frequency of extreme heat days is generally widespread across the biome, although with notable regional variations. These regional differences exhibit a strong and distinct seasonal pattern, as illustrated in Fig. 8 . During the warmer seasons, spring (SON) and summer (DJF), there is a pronounced increase in event frequency, with the highest intensity observed in the northern and central portions of the Pantanal. In spring, this positive anomaly is particularly critical, exceeding 4 events in the northern part of the biome. In contrast, during autumn (MAM) and, especially, winter (JJA), the model indicates a tendency toward a reduction in the frequency of these events. This decrease is most pronounced in the southern Pantanal and along its eastern edge, where negative anomalies reach approximately − 1 event. This spatial contrast suggests the presence of a north–south dipole pattern, reflecting the coexistence of areas experiencing both intensification and attenuation of extreme heat within the biome. For the intermediate-emission scenario SSP2-4.5, the seasonal warming trend intensifies while maintaining a similar spatial pattern, as illustrated in Fig. 9 . In spring (SON), the spatial pattern is most clearly defined, with a pronounced north–south gradient. The largest increase in event frequency, exceeding 5 units, is concentrated across the northern half of the biome, gradually weakening toward the south. During summer (DJF), a similar, though more subtle, pattern is projected: increases of up to 2 events are almost entirely confined to the northern third of the Pantanal, while the central and southern portions exhibit minimal changes, close to neutral conditions. In contrast, winter (JJA) is characterized by a negative anomaly, with the greatest intensity located in the central-southern portion and along the eastern edge of the biome, where reductions in events reach − 1. On the western edges and in the far north, the anomaly is less pronounced. Finally, autumn (MAM) is notable for its spatial homogeneity, with near-neutral conditions prevailing across almost the entire floodplain, except for a very minor localized increase in the far northern Pantanal. Under the high-emission scenario SSP5-8.5, projections indicate a pronounced exacerbation of heat anomalies, with an increase in the frequency of extreme events throughout the year (Fig. 10 ). The attenuation trend observed during autumn and winter in the previous scenarios is completely reversed, and all seasons now exhibit positive anomalies, highlighting persistent thermal stress across the biome. Spring (SON) and autumn (MAM) emerge as the most critical periods, with projected increases of up to 7 and 4 events, respectively. Spatially, the largest changes are consistently concentrated in the northern and central portions of the Pantanal, establishing these areas as future warming hotspots. Even winter (JJA) shows an increase in event frequency, indicating the near elimination of the biome’s milder seasonal conditions under this extreme-emission scenario. The Pantanal biome does not constitute a homogeneous landscape; rather, it is a complex mosaic of distinct sub-regions, each with particularities in flood regime, topography, and vegetation structure. The classification by Silva & Abdon (1998), which delineates 11 sub-regions, is the most widely recognized reference for understanding this internal diversity. When future scenarios are examined through this regional lens, these sub-regions can be grouped into distinct climate-response zones, allowing for a deeper understanding of the biome’s vulnerability. The spatial distribution of anomalies projects a future of uneven warming. In the far northern portion of the biome, the sub-regions of Cáceres, Poconé, and Barão de Melgaço form a clear hotspot, expected to experience the most consistent and severe increases in the frequency of heat events. Immediately to the south, the vast central plains of Paiaguás and Nhecolândia display a similarly high-impact pattern. Adjacent to these, the western river corridor encompassing the sub-regions of Paraguai, Abobral, Aquidauana, and Miranda also projects significant increases, albeit generally of slightly lower magnitude, functioning as a major transition zone. Finally, a distinct pattern emerges in the southwest, where Porto Murtinho and Nabileque consolidate as the areas of lowest overall warming; here, the phenomenon of seasonal attenuation, with reductions in winter heat events, is most pronounced under low-emission scenarios. Seasonal analysis Even more revealing than the annual increase is the profound reconfiguration of extreme heat seasonality. Table 4 summarizes the distribution of days in heatwaves across the seasons, highlighting a shift in the annual climate regime. Table 4 , Average number of days per year in heatwaves (fixed threshold), aggregated by season. Scenario Summer (DJF) Autumn (MAM) Winter (JJA) Spring (SON) Total NC 1.43 2.70 1.62 2.45 8.20 SSP1-1.9 10.46 11.30 1.49 23.30 46.55 SSP2-4.5 10.17 12.41 1.18 25.20 48.96 SSP5-8.5 14.15 18.35 3.92 31.39 67.81 In summary, the results indicate a future in which the climate of the Pantanal is not only warmer in its annual mean but is fundamentally restructured (Fig. 11 ). The projected change creates a significantly longer and more intense “heat season,” which begins with greater intensity in spring and extends through autumn, drastically altering the thermal regime to which the region’s biodiversity and human activities are currently adapted. The analysis of Fig. 6 and Table 4 reveals three key findings: 1. Spring (SON) as the Epicenter of Change: The austral spring emerges as the new epicenter of extreme heat. The number of heatwave days during this season rises sharply from just 2.5 to over 31 under the SSP5-8.5 scenario, representing more than a tenfold increase. This transforms a season that formerly marked the transition into warmth into a period of prolonged and intense thermal stress. 2. Expansion of the “Hot Season” into Autumn (MAM): Autumn, historically characterized by few extreme heat days, becomes a second critical season. With an increase of up to nearly sevenfold (from 2.7 to 18.4 days under SSP5-8.5), the “hot season” effectively extends, reducing the window of milder temperatures. 3. Disruption of the Winter Pattern (JJA): Although winter remains the season with the lowest activity, the SSP5-8.5 scenario already indicates a notable rise (from 1.6 to nearly 4 days). This signal, even if still moderate, is significant, as it shows that under high emissions, not even the coldest season will be immune to the occurrence of positive-temperature extremes. Discussion Climate change refers to significant and long-term alterations in the statistical properties of the climate system, including its mean state and variability (IPCC, 2021). Empirical evidence indicates that the Earth's average surface temperature has increased by approximately 0.71°C over the past century (Arfasa et al., 2024), primarily due to anthropogenic greenhouse gas emissions. According to the IPCC, global warming of between 1.5°C and 5.8°C is projected throughout the 21st century, increasing the frequency and intensity of extreme events such as heatwaves, floods, and droughts (Wu et al., 2016). These global tendencies directly influence regional hydroclimatic systems, particularly in sensitive biomes such as the Pantanal, where temperature and precipitation changes are expected to drive substantial ecological and hydrological transformations. Recent hydroclimatic analyses have provided new insights into the global–regional coupling mechanisms that regulate precipitation variability in the Upper Paraguay River Basin and the Pantanal. Thielen et al. (2020) demonstrated that sea surface temperature (SST) anomalies in the North Atlantic and North Pacific Oceans explain up to 80% of the interannual variance in Pantanal precipitation, with a two-month lag between oceanic warming and rainfall response. This teleconnection pattern indicates that persistent SST warming in the Northern Hemisphere amplifies the likelihood of severe and prolonged droughts, while certain regions of the Southern Hemisphere (South Atlantic, South Pacific, and Indian Oceans) show positive correlations with enhanced precipitation. These results emphasize that the Pantanal’s hydroclimatic variability is strongly influenced by remote ocean–atmosphere interactions, which modulate the alternation between flood and drought phases. Consequently, under current global warming trends, the intensification of SST anomalies may lead to more frequent and extreme hydroclimatic oscillations, exacerbating the biome’s exposure to compound drought–heat events already identified in recent years. The results of this study indicate an unprecedented intensification of heatwaves in the Pantanal between 2030 and 2060, revealing an increase of 126% to 181% in the number of days exceeding the extreme heat threshold, along with a significant expansion of the “hot and dry season.” These findings, derived from a fixed threshold anchored in the 1991–2020 climatological normal, demonstrate that events currently considered rare are likely to become common conditions in the near future. This behavior confirms that the biome is moving toward a new thermal regime in which the frequency and duration of heatwaves will no longer be exceptions but will instead structure the regional climatic seasonality. This result converges with recent evidence showing that the Pantanal has already been undergoing, since 2019, a climatic transition characterized by compound drought and heat events (Costa et al., 2024; Libonati et al., 2022; Marengo et al., 2021). The 2019–2021 period marked a turning point when multi-year droughts and heatwaves acted synergistically, amplifying fire risk and leading to severe environmental losses. In 2020, maximum temperatures exceeded the climatological mean by approximately 6°C during the peaks of these compound events, resulting in unprecedented thermal and hydrological stress (Costa et al., 2024). This land–atmosphere coupling, in which dry vegetation reinforces warming and vice versa, provides the physical background that explains both the record-breaking fires and the projected pattern of future intensification. From a dynamic perspective, the projection of concentrated heatwave events during spring and autumn confirms the reorganization of thermal seasonality observed in recent data. Miranda et al. (2025) identified that the September–November (SON-2020) quarter represented the peak of thermal and hydric stress, with a marked reduction in the evaporative fraction and an increase in surface temperature amplitude. These energy balance diagnostics support the hypothesis that the Pantanal climate system is shifting toward a regime dominated by greater heat persistence and reduced hydrological buffering, conditions that favor limited evapotranspiration and energy accumulation at the surface. Consequently, the projected pattern of longer and more frequent heatwaves during seasonal transitions reflects a redistribution of the regional energy balance, with direct implications for ecological and hydrological cycles. Although fire is a recurrent element in parts of the Pantanal’s grassland and savanna formations, its behavior and impact are strongly modulated by the hydrological regime of the floodplain. The annual flood pulse acts as a natural limiter to fire spread during the wet season and conditions vegetation recovery in the subsequent rainy period by restoring soil moisture and promoting regrowth. Conversely, years of prolonged drought and low river levels weaken this buffering effect, allowing fires to reach areas usually protected, such as forested patches and gallery forests, and increasing both the severity and extent of burns (Garcia et al., 2021; Marengo et al., 2021; Pelissari et al., 2023). In 2020, for instance, annual rainfall was approximately 26% below the 1982–2020 average, and the number of active fire detections increased by 123% compared to the 2002–2020 mean. Fires affected roughly one-third of the Pantanal's total area (~ 40,000 km²), including about 28% of wetlands that dried out due to drought conditions (Mataveli et al., 2021). These observations provide quantitative evidence of how hydrological disruption and thermal intensification interact to amplify fire activity across the floodplain. The multiscale analysis by Costa et al. (2024) showed that the 2019–2021 heatwave and drought covered up to 89% of the Pantanal on a 12-month scale, reinforcing that the combined effects are more relevant than isolated ones. Similarly, Ribeiro et al. (2022) demonstrated that the co-occurrence of high atmospheric dryness (elevated VPD) and low precipitation was the main driver of fires in both the Pantanal and Xingu, with a historical 5–10% increase in fire risk associated with compound events. These results suggest that, under global warming, the probability of “hot and dry” states will substantially increase, even under mitigation scenarios, aligning with the projections presented here. Thus, the future increments in WSDI and HWFI projected in this study are not merely statistical artifacts but represent a tangible physical risk of intensifying coupling between heat, dryness, and flammability. The year 2020, considered the most severe environmental disaster ever recorded in the Pantanal (Garcia et al., 2021; Pelissari et al., 2023), provides an empirical analogy of what the future may resemble under SSP2-4.5 and SSP5-8.5 scenarios. Pelissari et al. (2023) reported 300,127 fire hotspots between 2001 and 2022, with 2020 showing the lowest precipitation and the highest fire occurrence and burn severity (high ΔNBR). Together, these analyses reveal that the 2019–2021 megadrought and heatwaves have already anticipated the mean conditions projected for 2030–2060. The transition detected in this study, from an annual average of 36 to up to 103 days above the 90th percentile temperature, therefore represents the consolidation of a regime already empirically underway. The ecological implications of this transformation are profound. In savanna and grassland ecosystems, fire plays a key ecological role in nutrient cycling and maintaining open vegetation structure, but altered intensity and frequency may exceed the adaptive tolerance of species (Valente & Laurini, 2024; Pivello et al., 2021). Analyses of burn severity (Pelissari et al., 2023) and productivity (GPP) indicate that vegetation resilience decreases drastically under hot and dry events, with heterogeneous recovery and phenological delays. Miranda et al. (2025) also reported a marked decline in evaporative fraction and CO₂ flux during 2018–2020, reinforcing the physiological impact of heat–drought interactions. Therefore, the projected increase in heatwaves is expected to intensify plant thermal and hydric stress, with direct implications for ecosystem flammability and productivity. From a biological standpoint, the effects extend beyond vegetation. The 2020 thermal collapse resulted in an estimated death of 17 million vertebrates, including endemic species (Tomas et al., 2021). Magioli et al. (2024) showed that even one year after the megafire, there was a decline in mammal richness and abundance in monospecific forests, evidencing that the recurrence of severe events may erode regional beta diversity. In the projected scenario, where heatwaves become seasonal and prolonged, Pantanal fauna will face direct thermal stress, habitat loss, and fragmentation of hydric refugia. Therefore, the projected thermal intensification represents not merely a climatic issue but a direct threat to the functional integrity of the biome’s biodiversity. The expansion of the “hot and dry season,” beginning in August and extending until April, also has critical implications for fire management and environmental planning. Recent studies by Couto et al. (2024, 2025) demonstrated that convective gusts and gust fronts during extremely hot and dry days can multiply fire propagation rates, especially when fine fuels are desiccated. The projected trend of more persistent heat implies greater overlap between periods of meteorological fire risk and the availability of flammable fuel, requiring structural changes in prescribed burning windows, brigade positioning, and monitoring calendars. The recommendation by Garcia et al. (2021) for a permanent Integrated Fire Management (MIF) program becomes even more urgent in light of the projections presented here. Spatial analysis in this study also indicates that the northern and central portions of the Pantanal (subregions of Cáceres, Poconé, Barão de Melgaço, Paiaguás, and Nhecolândia) will be the most affected by the increase in heatwaves, coinciding with areas that historically show the highest fire severity (Pelissari et al., 2023; Valente & Laurini, 2024). This pattern reinforces the need for regionally differentiated management and conservation strategies that integrate thermal risk indicators (WSDI, HWFI) with hydrological and vegetation metrics. Altogether, the results point to three fundamental mechanisms driving the Pantanal’s transition toward a new climatic regime: (1) a positive feedback between vegetation desiccation and surface warming, (2) the weakening of seasonal hydrological buffering that traditionally mitigated heat extremes, and (3) the extension of the meteorological fire-risk period into what was formerly the wet season. This triad defines the structural shift that underpins the observed and projected intensification of compound hot–dry conditions across the biome. Finally, the results confirm the central hypothesis that the Pantanal is moving toward a new climatic regime in which extreme heat will not be an episodic event but a structuring condition. Even under the most optimistic scenario (SSP1-1.9), projections indicate that nearly one-quarter of the year will occur under extreme heat conditions, profoundly transforming ecological cycles, agricultural practices, and the socio-environmental security of the biome. The evidence converges on the urgent need for mitigation and adaptation policies that combine high-resolution climate monitoring, the strengthening of local firefighting brigades, and the integration of scientific knowledge with traditional wisdom in territorial planning for the Pantanal. Conclusions These findings illustrate that the Pantanal’s transition toward a new heat-dominated regime represents a paradigmatic case of regional environmental change. The intensification of heatwaves and the expansion of the dry–hot season point to systemic feedbacks between climate, hydrology, and fire regimes. This has profound implications for land-use planning, conservation policy, and adaptive fire management, emphasizing the need for regionally tailored climate resilience strategies The reference climatology (1991–2020) already reflects a world profoundly altered by climate change, with intensified thermal extremes compared to the pre-industrial period. Even the so-called “baseline climate” does not represent stable or natural conditions, but an already transformed state of the global climate system. Among the evaluated models, the Japanese MRI-ESM2-0 showed the best performance in reproducing daily maximum temperatures over the Pantanal. Projections based on this altered baseline indicate a pronounced intensification of heatwaves under future climate scenarios (2030–2060), with an increase of 126% to 181% in extreme heat days and a marked expansion of the “hot and dry season.” Conditions currently considered exceptional will become common, fundamentally restructuring the region’s thermal regime. The austral spring emerges as the new epicenter of extreme heat, while autumn experiences a notable extension of high-temperature conditions. Even winter, historically the mildest season, is projected to show substantial increases in extreme events under high-emission scenarios. Spatial analysis reveals that northern and central subregions will experience the most severe and persistent heat, highlighting the heterogeneous nature of climate impacts across the biome. These projected changes have profound ecological and socio-environmental implications. Intensified heatwaves are likely to exacerbate fire risk, reduce vegetation resilience, disrupt ecological cycles, and threaten biodiversity, including endemic species. Overlaps with existing droughts may further challenge agriculture, water management, and human well-being. The findings underscore the urgent need for integrated mitigation and adaptation strategies, including high-resolution climate monitoring, targeted fire management, and ecosystem-based approaches to safeguard both biodiversity and socio-environmental security. Beyond climatic implications, these results reveal a profound bioclimatic restructuring of the Pantanal, with potential impacts on species adaptation, ecosystem resilience, and fire regimes. In summary, the Pantanal is entering a new climatic regime in which extreme heat is not merely episodic but a structuring feature of the regional climate. These changes expose the biome to compounded risks from heat, drought, and fires, emphasizing the critical need for proactive measures to preserve ecosystem resilience and regional sustainability. Declarations Funding This work was supported by [nome da agência de fomento] (Grant number: [número do projeto]). Competing Interests The authors declare that they have no relevant financial or non-financial interests related to this work. Author Contributions The manuscript was conceived and developed by [Autor A]. All other authors reviewed the manuscript and contributed to the writing of the final version. Data Availability The data supporting the findings of this study are available from the authors upon reasonable request. References ALHO CJR, Sabino J (2012) Seasonal Pantanal flood pulse: implications for biodiversity conservation, a review. Oecologia Australis 16(4):958–978 ARFASA E M, TESFAYE B, GETAHUN M, ALEMU A D, BEKELE M (2024) Climate change and variability impacts on hydroclimatic resources: a global synthesis. Environ Earth Sci **83:**1–15. https://doi.org/10.1007/s12665-024-11829-3 COSTA MC, Marengo JA, Alves LM, Cunha AP (2024) Multiscale analysis of drought, heatwaves, and compound events in the Brazilian Pantanal in 2019–2021. Theoretical and Applied Climatology 155:661–677. https://doi.org/10.1007/s00704-023-04731-1 COUTO FT et al (2024) A case study of the possible meteorological causes of unexpected fire behavior in the Pantanal wetland, Brazil. Earth 5(3):548–563. https://doi.org/10.3390/earth5030032 COUTO FT et al (2025) Exploratory analysis of atmospheric modelling use over Pantanal wildfires. RA’EGA – O Espaço Geográfico em Análise 63(2):35–56 FERON S, Cordero RR, Damiani A, MacDonell S, Pizarro J, Goubanova K, Valenzuela R, Wang C, Rester L, Beaulieu A (2024) South America is becoming warmer, drier, and more flammable. Communications Earth & Environment 5:501. https://doi.org/10.1038/s43247-024-01289-y GARCIA LC et al (2021) Record-breaking wildfires in the world’s largest continuous tropical wetland: integrative fire management is urgently needed for both biodiversity and humans. Journal of Environmental Management 293:112870. https://doi.org/10.1016/j.jenvman.2021.112870 HAMILTON SK (2002) Hydrological controls of ecological structure and function in the Pantanal wetland (Brazil). In: McClain ME (ed) The Ecohydrology of South American Rivers and Wetlands . IAHS Special Publication 6:133–158 IPCC (2021) Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge LIBONATI R, Dacamara CC, Peres LF, Sander de Carvalho LA, Garcia LC (2020) Rescue Brazil’s burning Pantanal wetlands. Nature 588:217–219. https://doi.org/10.1038/d41586-020-03441-7 LIBONATI R et al (2022) Assessing the role of compound drought and heatwave events on unprecedented 2020 wildfires in the Pantanal. Environmental Research Letters 17(1):015005. https://doi.org/10.1088/1748-9326/ac3a5a MATAVELI G A V, Pereira G, de Oliveira G, Seixas H T, Cardozo F S, Shimabukuro Y E, Kawakubo F S, Brunsell N A (2021) 2020 Pantanal’s widespread fire: short- and long-term implications for biodiversity and conservation. Biodivers Conserv **30:**3299–3303. https://doi.org/10.1007/s10531-021-02243-2 MAGIOLI M et al (2024) Forest type modulates mammalian responses to megafires. Scientific Reports 14:13538. https://doi.org/10.1038/s41598-024-61823-8 MARENGO JA et al (2021) Extreme drought in the Brazilian Pantanal in 2019–2020: characterization, causes, and impacts. Frontiers in Water 3:639204. https://doi.org/10.3389/frwa.2021.639204 MIRANDA V et al (2025) Evapotranspiration anomalies over the Pantanal region during the droughts 2018–2020. Recent Advances in Remote Sensing NETO JBF, Carpenedo CB, Pereira G (2026) Ondas de calor e de frio em clima tropical de altitude: evidências entre 1994 e 2060. Revista Territorium Terram 9(Special Issue 1) PELISSARI TD et al (2023) Dynamics of major environmental disasters involving fire in the Brazilian Pantanal. Scientific Reports 13:21669. https://doi.org/10.1038/s41598-023-49074-4 PIVELLO VR et al (2021) Understanding Brazil’s catastrophic fires: causes, consequences and policy needed to prevent future tragedies. Perspectives in Ecology and Conservation 19:233–255. https://doi.org/10.1016/j.pecon.2021.06.002 PLETSCH MAJS, Silva Junior CHL, Penha TV, Körting TS, Silva MES, Pereira G, Anderson LO, Aragão LEOC (2021) The 2020 Brazilian Pantanal fires. Anais da Academia Brasileira de Ciências 93(3):e20210077. https://doi.org/10.1590/0001-3765202120210077 RAMOS RC, Pereira G, Galvani E, Cardozo FS, Santos PR, Dutra SB (2025) Dinâmica dos eventos de friagem na região do Pantanal: um estudo com dados de reanálise de 1981 a 2023. Revista Brasileira de Climatologia 37(21):138–162. https://doi.org/10.55761/abclima.v37i21.19967 REBOITA MS, Ferreira GWS, Ribeiro JGM, Ali S (2024) Assessment of precipitation and near-surface temperature simulation by CMIP6 models in South America. Environmental Research: Climate 3(2):025011. https://doi.org/10.1088/2752-5295/ad3b4b RIBEIRO AFS et al (2022) A compound event-oriented framework to tropical fire risk assessment in a changing climate. Environmental Research Letters 17(6):065015. https://doi.org/10.1088/1748-9326/ac6da5 SANTOS DM et al (2024) Compound dry-hot-fire events connecting Central and Southeastern South America: an unapparent and deadly ripple effect. npj Natural Hazards 1:32. https://doi.org/10.1038/s44298-024-00033-8 SCHÄR C et al (2004) The role of increasing temperature variability in European summer heatwaves. Nature 427:332–336. https://doi.org/10.1038/nature02300 SENEVIRATNE SI et al (2021) Weather and climate extreme events in a changing climate. In: Climate Change 2021: The Physical Science Basis. Cambridge University Press, Cambridge, pp 1513–1766 SHIMABUKURO YE, de Oliveira G, Pereira G, Arai E, Cardozo F, Dutra AC, Mataveli G (2023) Assessment of burned areas during the Pantanal fire crisis in 2020 using Sentinel-2 images. Fire 6(7):277. https://doi.org/10.3390/fire6070277 SILVA PSV et al (2024) Joining forces to fight wildfires: science and management in a protected area of Pantanal, Brazil. Environmental Science and Policy 159:103818. https://doi.org/10.1016/j.envsci.2024.103818 SILVA JSV, Abdon MM (1998) Delimitação do Pantanal brasileiro e suas sub-regiões. Pesquisa Agropecuária Brasileira 33(Suppl):1703–1711 THIELEN, D., SCHUCHMANN, K.-L., RAMONI-PERAZZI, P., MÁRQUEZ, M., ROJAS, W., QUINTERO, J. I., & MARQUES, M. I. M. (2020). Quo vadis Pantanal? Expected precipitation extremes and drought dynamics from changing sea surface temperature. PLOS ONE, 15(1), e0227437. https://doi.org/10.1371/journal.pone.0227437) THOMAS NP, Marquardt Collow AB, Bosilovich MG, Dezfuli A (2023) Effect of baseline period on quantification of climate extremes over the United States. Geophysical Research Letters 50:e2023GL105204. https://doi.org/10.1029/2023GL105204 TOMAS WM et al (2019) Sustainability agenda for the Pantanal wetland: perspectives on a collaborative interface for science, policy, and decision-making. Tropical Conservation Science 12:1–30. https://doi.org/10.1177/1940082919872634 TOMAS WM et al (2021) Distance sampling surveys reveal 17 million vertebrates directly killed by the 2020's wildfires in the Pantanal, Brazil. Scientific Reports 11:23547. https://doi.org/10.1038/s41598-021-02611-0 VALENTE F, Laurini M (2024) The dynamics of fire activity in the Brazilian Pantanal: a log-Gaussian Cox process-based structural decomposition. Fire 7(5):170. https://doi.org/10.3390/fire7050170 WU T, ZHANG J, LI W, LIU Y, LI L (2016) Global climate projections under different emission scenarios: summary for policymakers. Clim Dyn **47:**321–336. https://doi.org/10.1007/s00382-016-2907-8 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 23 Feb, 2026 Read the published version in Bulletin of Atmospheric Science and Technology → Version 1 posted Editorial decision: Revision requested 27 Dec, 2025 Reviews received at journal 27 Dec, 2025 Reviews received at journal 23 Dec, 2025 Reviewers agreed at journal 01 Dec, 2025 Reviewers agreed at journal 27 Nov, 2025 Reviewers invited by journal 27 Nov, 2025 Editor assigned by journal 21 Nov, 2025 Submission checks completed at journal 21 Nov, 2025 First submitted to journal 14 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8119100","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":551797504,"identity":"8912289d-6e02-43e5-9b23-e65bf7f34bef","order_by":0,"name":"João Batista Ferreira 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16:18:14","extension":"html","order_by":26,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":103105,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8119100/v1/d938807dd985c7f9fbd4c864.html"},{"id":97368059,"identity":"644bc9d0-1cb9-4657-8e6d-631f56345810","added_by":"auto","created_at":"2025-12-03 16:21:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1963108,"visible":true,"origin":"","legend":"\u003cp\u003eLocation and relief map. The analysis area (black outline) covers the Pantanal floodplain. The location of Corumbá, the main urban center and capital of the Pantanal, is indicated by the star.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-8119100/v1/3facf7bd727e04140466d080.png"},{"id":97262647,"identity":"02764504-0ed4-4e0b-8435-1815761e2e45","added_by":"auto","created_at":"2025-12-02 14:09:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":37001,"visible":true,"origin":"","legend":"\u003cp\u003ePerformance of CMIP6 models in representing daily maximum temperature (2015–2024) compared to ERA5 reanalysis in the Pantanal.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-8119100/v1/9387f3f26a9b6ff426986237.png"},{"id":97262649,"identity":"b3a6a116-2535-4d55-ab2a-2acc3f64e366","added_by":"auto","created_at":"2025-12-02 14:09:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":33574,"visible":true,"origin":"","legend":"\u003cp\u003eComparative performance of CMIP6 models in representing temperature extremes (TXx and WSDI) relative to ERA5 reanalysis for the Pantanal (2015–2024).\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-8119100/v1/cf0d30195ea8f20893b93707.png"},{"id":97262663,"identity":"7e0da983-2f71-49f1-bbf4-710ff225c59b","added_by":"auto","created_at":"2025-12-02 14:09:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":47567,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between daily maximum temperatures simulated by MRI-ESM2-0 and observed in ERA5 reanalysis for the Pantanal (2015–2024).\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-8119100/v1/49a8c2cbe708e7674e117c83.png"},{"id":97262655,"identity":"b69f77b1-0ff8-4304-9530-74dd87e43b77","added_by":"auto","created_at":"2025-12-02 14:09:06","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":30838,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal similarity analysis among SSP climate scenarios for the Pantanal.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-8119100/v1/3180287f784413005af074c6.png"},{"id":97367642,"identity":"dc372991-ddb5-4cc2-bc39-bf8f12e2784c","added_by":"auto","created_at":"2025-12-03 16:19:56","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":48301,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual mean number of days in heatwaves.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-8119100/v1/5c19c17203447f0f713086d4.png"},{"id":97367627,"identity":"28000f42-27f0-4aa5-bde0-734106a7fa2b","added_by":"auto","created_at":"2025-12-03 16:19:50","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":45687,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual cycle of heatwave days in the Pantanal for the historical period and future scenarios.\u003c/p\u003e\n\u003cp\u003eThe greatest concentration of events occurs during the austral spring. October emerges as the seasonal peak in all scenarios, with the mean number of heatwave days increasing from approximately 1.1 in the reference period to 9.3 (SSP1-1.9), 10.6 (SSP2-4.5), and 11.6 (SSP5-8.5).\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-8119100/v1/c8b6cbd32e8ba859af90f876.png"},{"id":97262676,"identity":"25f070df-e351-464d-a68e-6ddefeda8186","added_by":"auto","created_at":"2025-12-02 14:09:06","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":446915,"visible":true,"origin":"","legend":"\u003cp\u003eSeasonal spatial variation in the frequency of extreme heat events in the Pantanal under the SSP1-1.9 scenario (2030–2060) relative to the historical period (1991–2020).\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-8119100/v1/c203aa0509da17d4ca03efdd.png"},{"id":97262667,"identity":"78b91531-e579-43f7-acbc-0e7e7b2613f3","added_by":"auto","created_at":"2025-12-02 14:09:06","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":408240,"visible":true,"origin":"","legend":"\u003cp\u003eSeasonal spatial variation in the frequency of extreme heat events in the Pantanal under the SSP2-4.5 scenario (2030–2060) relative to the historical period (1991–2020).\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-8119100/v1/e8543b470b7b5b8ac74a4170.png"},{"id":97262661,"identity":"f4353bc4-47c4-4e73-a489-bfd9b185ea10","added_by":"auto","created_at":"2025-12-02 14:09:06","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":441053,"visible":true,"origin":"","legend":"\u003cp\u003eSeasonal spatial variation in the frequency of extreme heat events in the Pantanal under the SSP5-8.5 scenario (2030–2060) relative to the historical period (1991–2020).\u003c/p\u003e","description":"","filename":"image10.png","url":"https://assets-eu.researchsquare.com/files/rs-8119100/v1/bf73b33b073549331bf2e7d5.png"},{"id":97367101,"identity":"957bf1c6-8f91-4481-8826-44d5e6c9ae9d","added_by":"auto","created_at":"2025-12-03 16:16:26","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":51946,"visible":true,"origin":"","legend":"\u003cp\u003eSeasonal average of heatwave days per year in the Pantanal, based on the TX90p threshold of the 1991–2020 climatological normal and projections for 2030–2060 under different SSP scenarios\u003c/p\u003e","description":"","filename":"image11.png","url":"https://assets-eu.researchsquare.com/files/rs-8119100/v1/86b2d1f216e6d3355948aaba.png"},{"id":103765550,"identity":"cd41b876-3488-4023-add1-ff8137319bb4","added_by":"auto","created_at":"2026-03-02 16:04:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4223106,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8119100/v1/8403c5d8-d6fc-4e13-aefd-a849a65732f9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Towards a new climate regime: heatwaves proliferate and reshape seasonality in the world's largest tropical wetland","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAnthropogenic global warming has increased the frequency, duration, and spatial extent of extreme climate events worldwide, including the simultaneous occurrence of heatwaves and droughts. When these extremes overlap, they create compound events whose synergistic effects significantly elevate wildfire risk and degrade air quality on regional and global scales (SANTOS et al., 2024). Luo et al. (2022) demonstrated that spatiotemporally contiguous heatwaves have risen markedly in frequency, magnitude, duration, and spatial extent across China since the 1960s, primarily due to intensified global and regional warming and the influence of large-scale atmospheric circulation patterns, such as Rossby waves and atmospheric blocking. Similarly, Zhang et al. (2025) emphasize that heatwaves often interact with other extreme phenomena, such as marine and coastal heat anomalies, resulting in amplified socio-environmental impacts. These findings reinforce the understanding that compound extremes, whether involving hydrological or thermal stress (such as compound drought\u0026ndash;heatwave events, or CDHW), are becoming more frequent and complex under anthropogenic warming, with cascading consequences for ecosystems and human health. In the Pantanal, these processes have become increasingly evident, as prolonged dry spells coupled with record-breaking heat intensify fire activity and the persistence of atmospheric pollutants.Understanding how heatwaves reshape thermal and hydrological regimes is essential not only for climate diagnostics but also for anticipating ecological and socio-environmental transformations. The Pantanal, as a keystone tropical wetland, provides an exemplary case of regional-scale climate reorganization under global warming.\u003c/p\u003e\u003cp\u003eAlthough South America has been identified as a hotspot for compound extreme events, their spatial patterns and impacts remain relatively under-documented in the scientific literature (Santos et al., 2024; Feron et al., 2024). Recent studies, however, highlight an intensification of these phenomena across southern South America. Trends observed in southeastern Brazil, where the frequency, intensity, and duration of hot and dry events have increased markedly (Perkins-Kirkpatrick \u0026amp; Lewis, 2020; Cunha et al., 2019), are highly relevant for the La Plata Basin, which encompasses the Pantanal. Within this critical basin, warm and dry conditions have become more frequent (Barrucand et al., 2014; Tencer et al., 2016), and recent evidence indicates a rise in compound precipitation\u0026ndash;temperature extremes (Olmo et al., 2020; Hao et al., 2018). In this context of increasing climate vulnerability, the Pantanal, the world\u0026rsquo;s largest continuous floodplain, stands out. Its structure and functioning are governed by a seasonal flood pulse, which generates a complex mosaic of aquatic and terrestrial habitats and supports remarkable biodiversity (ALHO; SABINO, 2012; TOMAS et al., 2019). Ecologically, the Pantanal is a fire-dependent biome, in which fire plays a crucial role in maintaining ecosystem structure and processes (PIVELLO et al., 2021). However, the biome faces severe threats, notably the conversion of native vegetation to pastures and croplands, particularly on adjacent plateaus where the headwaters of its rivers are located (ALHO; SABINO, 2012; MARENGO et al., 2021). Furthermore, the Pantanal\u0026rsquo;s sensitivity to abrupt atmospheric disturbances is evident not only through heat extremes but also through cold snaps, which historically have caused significant socio-economic and environmental impacts, including livestock mortality and increased incidence of respiratory diseases (RAMOS et al., 2025).\u003c/p\u003e\u003cp\u003eDrought conditions render vegetation highly flammable, while heatwaves, with extreme temperatures and low humidity, elevate flammability to critical levels (LIBONATI et al., 2022). The 2020 fire season, which consumed approximately one-third of the biome (PLETSCH et al., 2021), exemplifies the extreme consequences of these combined factors. SHIMABUKURO et al. (2023) estimated that 44,998 km\u0026sup2; burned in the Brazilian portion, resulting from the concurrence of drought and persistent thermal anomalies (LIBONATI et al., 2020; MARENGO et al., 2021; PIVELLO et al., 2021). Studies indicate that the co-occurrence of these compound drought\u0026ndash;heatwave events (CDHW) accounted for more than 70% of the affected area (LIBONATI et al., 2022; SANTOS et al., 2024). This process led to the release of over 115\u0026nbsp;million tons of CO₂ and the estimated death of 17\u0026nbsp;million vertebrates, including endemic and threatened species (TOMAS et al., 2021; SHIMABUKURO et al., 2023). Overall, heatwaves emerge as a primary driver of wildfire intensification in the Pantanal.\u003c/p\u003e\u003cp\u003eDespite advances in understanding these dynamics, substantial knowledge gaps remain, particularly regarding the quantification of future changes in extreme temperature regimes and their implications for environmental management and planning (LIBONATI et al., 2020; PIVELLO et al., 2021). Given the biome\u0026rsquo;s intrinsic vulnerability, where extreme heat already acts as a trigger for ecological disasters, the central hypothesis of this study is that global warming is inducing a fundamental restructuring of the Pantanal\u0026rsquo;s climate regime. It is hypothesized that heatwaves, as defined by historical climatology (1991\u0026ndash;2020), will shift from sporadic events to a chronic and dominant seasonal feature in the near future (2030\u0026ndash;2060).\u003c/p\u003e\u003cp\u003eTo test this hypothesis and assess the magnitude of this transformation, the main objective of this study is to quantify and characterize changes in the frequency, duration, and seasonality of heatwaves in the Pantanal under a near-future scenario (2030\u0026ndash;2060), across different socioeconomic development pathways (SSPs), using a fixed climatological threshold from the historical period (1991\u0026ndash;2020) as reference.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eSpatial and temporal delimitation\u003c/h2\u003e\u003cp\u003eThe temporal analysis was based on two 30-year climatological periods. The historical reference period was defined as 1991\u0026ndash;2020, in accordance with the latest standard climatological normal established by the World Meteorological Organization (WMO). Adhering to this international guideline ensures that the results are methodologically comparable with climate studies and reports at a global scale. For projections, the period 2030\u0026ndash;2060 was selected, maintaining the same 30-year duration to ensure a statistically balanced and consistent comparison between past climate conditions and those expected in the near future.\u003c/p\u003e\u003cp\u003eThe spatial domain of the study is the Pantanal biome (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Its delineation was performed in two steps: first, for data acquisition via Application Programming Interface (API), a broad rectangular area was defined (14\u0026deg;S to 23\u0026deg;S, 60\u0026deg;W to 53\u0026deg;W). Subsequently, for analyses, the final spatial subset was adjusted to the official biome boundaries using its vector polygon (shapefile), with an external one-pixel buffer applied to mitigate edge effects on the results.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData sources and climate scenarios\u003c/h3\u003e\n\u003cp\u003eDaily maximum temperature (tasmax) data were obtained from two main sources:\u003c/p\u003e\u003cp\u003eHistorical Period (1991\u0026ndash;2020): ERA5 reanalysis dataset (reanalysis-era5-single-levels), provided by the European Centre for Medium-Range Weather Forecasts (ECMWF).\u003c/p\u003e\u003cp\u003eFuture Period (2030\u0026ndash;2060): Climate projections were derived from Earth System Models (ESMs) participating in the sixth phase of the Coupled Model Intercomparison Project (CMIP6). For validation, four distinct models were considered: CAMS-CSM1-0 (China), EC-Earth3-Veg (Europe), MRI-ESM2-0 (Japan), and GFDL-ESM4 (USA).\u003c/p\u003e\u003cp\u003eProjections were analyzed under three Shared Socioeconomic Pathways (SSPs), which describe alternative plausible socioeconomic futures and the resulting greenhouse gas emissions trajectories:\u003c/p\u003e\u003cp\u003eSSP1-1.9: Represents a sustainability and low-emission scenario. This optimistic pathway is characterized by global development prioritizing well-being and equity, strong climate mitigation, rapid transition to renewable energy, and international cooperation, aligning with the goal of limiting global warming to 1.5\u0026deg;C above pre-industrial levels (O\u0026rsquo;Neill et al., 2016).\u003c/p\u003e\u003cp\u003eSSP2-4.5: Known as the intermediate or \u0026ldquo;middle-of-the-road\u0026rdquo; scenario. It describes a world following historical development patterns, with uneven progress and moderate climate policies. GHG emissions continue to rise in the near term, stabilize mid-century, and gradually decline thereafter, resulting in a radiative forcing of 4.5 W/m\u0026sup2; by 2100 (O\u0026rsquo;Neill et al., 2016).\u003c/p\u003e\u003cp\u003eSSP5-8.5: Corresponds to a high-emission scenario driven by fossil-fuel-intensive development. This pathway describes rapid global economic growth, fossil-based energy exploitation, and a high-consumption lifestyle, resulting in continuously increasing emissions and the most extreme warming scenario (radiative forcing of 8.5 W/m\u0026sup2; by 2100) (O\u0026rsquo;Neill et al., 2016).\u003c/p\u003e\n\u003ch3\u003eData preprocessing and harmonization\u003c/h3\u003e\n\u003cp\u003eNetCDF data underwent preprocessing to ensure consistency and comparability:\u003c/p\u003e\u003cp\u003eUnit and Calendar Conversion: Temperatures were converted from Kelvin (K) to Celsius (\u0026deg;C). All time series were standardized to a non-leap-year Gregorian calendar (365 days/year).\u003c/p\u003e\u003cp\u003eSpatial Harmonization: To ensure spatial comparability, MRI-ESM2-0 model data were regridded to the native ERA5 grid (0.25\u0026deg; \u0026times; 0.25\u0026deg;) using a first-order conservative remapping method. This step guarantees that subsequent analyses are performed on an identical spatial grid.\u003c/p\u003e\n\u003ch3\u003eClimate model validation and selection\u003c/h3\u003e\n\u003cp\u003eTo assess CMIP6 model performance in reproducing daily maximum temperature (tasmax) over the Pantanal, model outputs were compared against ERA5 reanalysis. The validation period was 2015\u0026ndash;2024, representing the most recent temporal overlap between observed reanalysis and the start of historical model simulations, allowing a direct evaluation of how well models replicate observed climate.\u003c/p\u003e\u003cp\u003ePerformance was quantified using multiple statistical metrics, each evaluating a distinct aspect of model skill:\u003c/p\u003e\u003cp\u003eMean Bias Error (MBE): Identifies systematic model bias, indicating whether the model overestimates (positive bias) or underestimates (negative bias) observed temperatures.\u003c/p\u003e\u003cp\u003eRoot Mean Square Error (RMSE): Measures the average magnitude of simulation errors, providing an overall measure of model accuracy. Unlike MBE, RMSE penalizes larger errors and does not indicate error direction.\u003c/p\u003e\u003cp\u003ePearson Correlation Coefficient (r): Quantifies the strength and direction of the linear relationship between simulated and observed values, evaluating how well the model captures variability and timing of temperature events.\u003c/p\u003e\u003cp\u003eKling-Gupta Efficiency (KGE): Provides an integrated diagnostic assessment, decomposing model performance into correlation, bias, and variability components. A KGE value close to 1 indicates near-perfect agreement between simulation and observation.\u003c/p\u003e\u003cp\u003eKolmogorov\u0026ndash;Smirnov (KS) Test: Compares cumulative probability distributions of simulated and observed data, assessing whether both samples come from the same distribution and verifying overall similarity in climatic variability beyond mean values.\u003c/p\u003e\u003cp\u003eAlthough multi-model ensembles are widely used to assess uncertainties, several recent studies (Reboita et al., 2024; Costa et al., 2024) have demonstrated that MRI-ESM2-0 consistently ranks among the top performers for South America, providing robust simulations of temperature extremes. Therefore, the use of this single, best-performing model ensures internal physical consistency across scenarios while maintaining computational tractability\u003c/p\u003e\n\u003ch3\u003eHeatwave definition and threshold strategy\u003c/h3\u003e\n\u003cp\u003eHeatwaves were identified following the methodology of the Expert Team on Climate Change Detection and Indices (ETCCDI). An event was defined as a period of six or more consecutive days during which daily maximum temperature (tasmax) exceeded the 90th percentile (P90), calculated for each calendar day from the reference climatology. Two threshold strategies were employed to ensure a comprehensive analysis:\u003c/p\u003e\u003cp\u003ePreliminary Analysis with Moving Threshold: Initially, the P90 was calculated independently for the historical period (1991\u0026ndash;2020) and each future scenario (2030\u0026ndash;2060). This approach allows the assessment of heat anomalies extreme relative to each period\u0026rsquo;s internal climate.\u003c/p\u003e\u003cp\u003eMain Analysis with Fixed Threshold: For the core analysis, a single P90 threshold was established using historical reference data only. This fixed threshold was then applied to all datasets (historical and future). This approach is essential to quantify absolute changes in frequency and duration of extreme heat days relative to current climate.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eIndices calculation\u003c/h2\u003e\u003cp\u003eUsing the fixed P90 threshold, two main ETCCDI indices were calculated to characterize heatwaves:\u003c/p\u003e\u003cp\u003eWarm Spell Duration Index (WSDI): Total number of days per year included in a heatwave event.\u003c/p\u003e\u003cp\u003eHeatwave Events (HWFI): Total number of events (\u0026ge;\u0026thinsp;6 consecutive days) occurring per year.\u003c/p\u003e\u003cp\u003eAnnual, monthly, and seasonal time series for both indices were generated for the historical period and each future SSP scenario, always using the same reference threshold to ensure direct comparability.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eComparative and statistical analysis\u003c/h3\u003e\n\u003cp\u003eComparisons between future and historical climates were performed by calculating absolute differences (Δ) for each index at each grid point:\u003c/p\u003e\u003cp\u003eΔ\u0026thinsp;=\u0026thinsp;FutureSSP\u0026thinsp;\u0026minus;\u0026thinsp;Past\u003c/p\u003e\u003cp\u003ePositive Δ values indicate increases in heatwave duration or frequency. Difference fields were exported as GeoTIFF files. In addition to spatial analysis, summary statistics, such as the spatial mean of indices over the Pantanal, were computed to obtain aggregated values representing projected biome-wide changes.\u003c/p\u003e\n\u003ch3\u003eSoftware and computational tools\u003c/h3\u003e\n\u003cp\u003eNetCDF file operations, including temporal aggregation, spatial subsetting, and index calculations, were conducted using Climate Data Operators (CDO) and NetCDF Operators (NCO). Workflow automation and integration were managed using Python scripts. All cartographic products and spatial visualizations were generated in QGIS (version 3.42).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003eClimate model validation and selection\u003c/h2\u003e\n\u003cp\u003e2.2 While multi-model ensembles provide a broader uncertainty envelope, the high skill of MRI-ESM2-0 in reproducing observed heat extremes supports the reliability of the projections presented here. The evaluation of selected CMIP6 models against the ERA5 reanalysis for the Pantanal region revealed clear differences in their ability to represent near-surface daily maximum temperature (Tmax) over the period 2015\u0026ndash;2024. Figure\u0026nbsp;2 summarizes the overall performance statistics (bias and RMSE) for the four models analyzed. Among them, MRI-ESM2-0 showed the best agreement with the ERA5 reference, with a mean bias of +\u0026thinsp;0.72\u0026deg;C and an RMSE of 1.48\u0026deg;C, substantially lower than those observed for CAMS-CSM1-0 (+\u0026thinsp;1.9\u0026deg;C; RMSE\u0026thinsp;=\u0026thinsp;2.8\u0026deg;C) and EC-Earth3-Veg (\u0026minus;\u0026thinsp;1.3\u0026deg;C; RMSE\u0026thinsp;=\u0026thinsp;1.9\u0026deg;C). GFDL-ESM4 also exhibited reasonable performance (bias\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.9\u0026deg;C; RMSE\u0026thinsp;=\u0026thinsp;2.1\u0026deg;C).\u003c/p\u003e\n\u003cp\u003eTemporal correlation analysis confirmed these results: MRI-ESM2-0 achieved the highest correlation coefficient (r\u0026thinsp;=\u0026thinsp;0.87) and the highest Kling-Gupta Efficiency (KGE\u0026thinsp;=\u0026thinsp;0.81), indicating excellent consistency in reproducing both the seasonal and interannual variability of Tmax. In contrast, CAMS-CSM1-0 systematically overestimated temperatures during the warm season, while EC-Earth3-Veg and GFDL-ESM4 tended to underestimate them.\u003c/p\u003e\n\u003cp\u003eWhen evaluating extreme temperature indices (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e), MRI-ESM2-0 again exhibited the best overall performance. The model simulated an annual maximum temperature (TXx) of 41.2\u0026deg;C, in excellent agreement with the ERA5 reference value of 41.5\u0026deg;C. The Warm Spell Duration Index (WSDI), which measures the number of days per year belonging to periods of at least six consecutive days with Tmax above the 90th percentile of the reference series (Alexander et al., 2006), was also well represented. MRI-ESM2-0 simulated an average of 58 days per year, compared to 63 days per year observed in ERA5. The other models showed larger discrepancies, ranging from 47 days/year (CAMS-CSM1-0) to 52 days/year (GFDL-ESM4).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe ability of MRI-ESM2-0 to represent heatwave events from 2019 to 2021, which coincided with severe drought and large-scale wildfires in the Pantanal, was particularly remarkable. As illustrated in Fig.\u0026nbsp;4, the scatter plot between daily Tmax from ERA5 and MRI-ESM2-0 shows a relationship close to the 1:1 line (r\u0026thinsp;\u0026asymp;\u0026thinsp;0.87), confirming that the model adequately reproduces both the magnitude and temporal dynamics of extreme heat days.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003ch2\u003e\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eOverall, these results justify the selection of MRI-ESM2-0 as the most reliable model for subsequent analyses. This choice is further supported by the literature, which highlights the model\u0026rsquo;s robustness in representing South American climate. The assessment by Reboita et al. (2024), which compared 50 CMIP6 models, identified MRI-ESM2-0 as one of the top-performing models for simulating temperatures in Brazil. Based on these findings, Neto et al. (2026) also selected the same model for a study on extremes in southern Minas Gerais.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n\u003ch2\u003eCoherence and divergence of ssp scenarios\u003c/h2\u003e\n\u003cp\u003eA key step in the analysis was to examine the interdependence among future climate trajectories, visualized in the correlation matrix in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. The results reveal divergence patterns consistent with the socioeconomic narratives underlying each pathway. Far from indicating model inconsistency, the correlations confirm the sensitivity of projections to the different forcing conditions imposed by each scenario.\u003c/p\u003e\n\u003cp\u003eIndividual coefficient analysis shows a clear hierarchy of association. The deep mitigation scenario, SSP1-1.9, demonstrates near-complete independence from the others. Its correlation with SSP2-4.5 is essentially zero (r\u0026thinsp;=\u0026thinsp;0.001) and with SSP5-8.5 is weakly positive (r\u0026thinsp;=\u0026thinsp;0.232), indicating that the adoption of stringent climate policies leads to a fundamentally distinct future trajectory.\u003c/p\u003e\n\u003cp\u003eConversely, the higher-emission scenarios, SSP2-4.5 and SSP5-8.5, exhibit a moderate positive correlation (r\u0026thinsp;=\u0026thinsp;0.545). This level of association suggests that, although distinct, these two pathways share greater structural similarity in their temporal variations, reflecting the climate physics of a world with insufficient or absent mitigation policies. Therefore, the matrix illustrates the progressive statistical divergence between the sustainable scenario and higher-warming trajectories, while validating the internal coherence of the model projections across different socioeconomic futures.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n\u003ch2\u003eHeatwaves with a moving threshold\u003c/h2\u003e\n\u003cp\u003eThe initial analysis, based on a moving threshold, provides a perspective on how heatwave anomalies evolve relative to the mean climate of each period, as summarized in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. In the high-emission scenario (SSP5-8.5), both the duration (+\u0026thinsp;18.26%) and frequency (+\u0026thinsp;9.03%) of events increase, indicating a future with more intense heat extremes even relative to an already warmer future climate.\u003c/p\u003e\n\u003cp\u003eHowever, a counterintuitive result emerges under the intermediate scenario (SSP2-4.5), which shows reductions in both duration (\u0026minus;\u0026thinsp;30.04%) and frequency (\u0026minus;\u0026thinsp;23.67%) of heatwaves. This apparent decrease does not indicate a milder future climate; rather, it is a methodological artifact inherent to using a moving threshold. As the background climate warms during 2030\u0026ndash;2060, the threshold defining \"extreme\" conditions (the 90th percentile) also rises. Consequently, it becomes statistically more difficult for days to exceed this new, higher baseline.\u003c/p\u003e\n\u003cp\u003eThis effect thus masks the increase in absolute thermal stress to which the biome will be exposed relative to historical conditions. To quantify the real impact of climate change relative to the present climate and overcome this interpretive limitation, a second analysis was conducted using a fixed threshold anchored in the 1991\u0026ndash;2020 reference period.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eHeatwave Index (HWFI) in the Pantanal (Moving Threshold). Comparison between the historical period (NC) and future scenarios (2030\u0026ndash;2060), showing totals for the 30-year period and annual averages in parentheses.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eScenario\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eHWFI -Days\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u0026Delta; vs. Hist\u0026oacute;rical\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u0026Delta;%\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eHWFI -Events\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u0026Delta; vs. Hist\u0026oacute;rical\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u0026Delta;%\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e273\u003c/p\u003e\n\u003cp\u003e(9/ano)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34 (1/ano)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSSP1-1.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e280\u003c/p\u003e\n\u003cp\u003e(9/ano)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e+\u0026thinsp;7,55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e+\u0026thinsp;2,77%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32 (1/ano)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1,88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-5,51%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSSP2-4.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e191\u003c/p\u003e\n\u003cp\u003e(7/ano)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-81,90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-30,04%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26 (1/ano)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-8,07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-23,67%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSSP5-8.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e323\u003c/p\u003e\n\u003cp\u003e(11/ano)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e+\u0026thinsp;49,77\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e+\u0026thinsp;18,26%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e38 (2/ano)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e+\u0026thinsp;3,08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e+\u0026thinsp;9,03%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eRecent studies show that when the climatological baseline is updated to a warmer period, the number of days classified as extremes decreases, even as warming continues (Thomas et al., 2023; Li, Zhou \u0026amp; Zhang, 2025). Thomas et al. (2023) demonstrated that in the United States, updating the climatology from 1981\u0026ndash;2010 to 1991\u0026ndash;2020 led to a marked reduction in the frequency of hot extremes and an apparent increase in cold extremes due to the elevated percentile threshold. Similar results were reported in China by Li, Zhou, and Zhang (2025), where the baseline shift delayed the \u0026ldquo;statistical emergence\u0026rdquo; of heat extremes, making them less frequent in the context of ongoing warming.\u003c/p\u003e\n\u003cp\u003eThis statistical behavior is consistent with the theory of temperature distribution shifts under global warming (Sch\u0026auml;r et al., 2004) and is recognized by the IPCC (2021) as an inherent effect of defining percentile-based extreme indices. Thus, even with an increase in mean temperatures, the number of days exceeding the 90th percentile is statistically lower, as the reference threshold shifts upward, requiring stronger anomalies to classify an event as extreme.\u003c/p\u003e\n\u003cp\u003eAs one of the main objectives is to establish a direct comparison with the historical climate, the threshold was kept fixed at the 1991\u0026ndash;2020 climatological baseline. Accordingly, the analyses in this stage were deliberately concluded here.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n\u003ch2\u003eHeatwaves with a fixed threshold\u003c/h2\u003e\n\u003cp\u003eIn contrast to the moving threshold analysis, the application of a fixed threshold, anchored in the historical climatology (1991\u0026ndash;2020 P90), reveals the true magnitude of future warming. Results show an increase in the number of extreme heat days in the Pantanal across all scenarios (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). What is currently considered an extreme heat day (averaging 36 days per year, or ~\u0026thinsp;10% of the time) will become much more frequent. In the future, the region will experience between 83 (SSP1-1.9) and 103 (SSP5-8.5) extreme heat days annually.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eAverage annual number of days with Tmax\u0026thinsp;\u0026ge;\u0026thinsp;P90 (fixed threshold 1991\u0026ndash;2020) and corresponding fraction of the year.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eScenario\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDays\u0026thinsp;\u0026ge;\u0026thinsp;P90 (days/yr)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFraction of Year (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e36,46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9,99\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSSP1-1.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e82,60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e22,63\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSSP2-4.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e85,84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e23,52\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSSP5-8.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e102,70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e28,14\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe magnitude of this change is substantial, as quantified in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. The Pantanal is projected to experience 46\u0026ndash;66 additional hot days per year relative to the present climate. In relative terms, this represents an increase of at least 126% (SSP1-1.9), rising to 181% under the most pessimistic scenario (SSP5-8.5). A clear \u0026ldquo;dose-response\u0026rdquo; relationship emerges: the higher the emission forcing of the scenario, the more severe and pronounced the increase in the frequency of extreme heat days.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eAbsolute and relative changes in the number of days\u0026thinsp;\u0026ge;\u0026thinsp;P90 relative to the historical period (1991\u0026ndash;2020).\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eScenario\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAbsolute Change (days/yr)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAbsolute Change (days/yr)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSSP1-1.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e+\u0026thinsp;46,14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e+\u0026thinsp;126,6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSSP2-4.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e+\u0026thinsp;49,38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e+\u0026thinsp;135,4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSSP5-8.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e+\u0026thinsp;66,24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e+\u0026thinsp;181,8\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eBeyond counting days\u0026thinsp;\u0026ge;\u0026thinsp;P90, we assessed heatwave occurrence. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e summarizes the annual change in the number of days within heatwaves (HWFI) and the number of events per year. The average number of heatwave days per year increases from 8.8 (historical) to 48.9 (SSP1-1.9), 50.7 (SSP2-4.5), and 69.6 (SSP5-8.5), while events per year rise from 1.1 to 4.9\u0026ndash;6.3. Thus, the future projects both more frequent and longer-lasting events.\u003c/p\u003e\n\u003cp\u003eFrom a climatological perspective, these results indicate a profound shift in the temperature regime, characterized by a strong expansion of the upper tail of the statistical distribution. Practically, this means that heat events currently considered rare or extreme will become a regular feature of the future climate. The fraction of the year with \u0026ldquo;very hot\u0026rdquo; days will increase from ~\u0026thinsp;10% to between 23% and 28%, i.e., nearly one-quarter of the year, even under the highest mitigation scenario. This \u0026ldquo;normalization\u0026rdquo; of extremes represents the clearest signature of intensified thermal stress that the Pantanal ecosystem will face in the coming decades.\u003c/p\u003e\n\u003cp\u003eThis projection is particularly alarming, as the Pantanal is a biome already operating under severe climate stress, with recent extreme events serving as a precursor of what is to come. The catastrophic fires of 2020, which devastated nearly one-third of the biome, were directly linked to a combination of severe drought and unprecedented heatwaves (Libonati et al., 2020).\u003c/p\u003e\n\u003cp\u003eAnnual aggregates indicate a general increase in heatwave occurrence, yet evaluation of seasonal distribution is required to understand the restructuring of the thermal regime. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e illustrates the monthly cycle of heatwave days, allowing comparison between historical patterns and future projections. The graphical representation shows that the historical pattern was characterized by a low incidence of heatwave days, with monthly averages rarely exceeding one day. In contrast, future scenarios indicate a marked concentration of events in the second half of the year. The transition begins in August, which, although historically registering minimal activity, shows a significant increase in hot days, marking the start of the period with highest incidence.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe greatest concentration of events occurs during the austral spring. October emerges as the seasonal peak in all scenarios, with the mean number of heatwave days increasing from approximately 1.1 in the reference period to 9.3 (SSP1-1.9), 10.6 (SSP2-4.5), and 11.6 (SSP5-8.5).\u003c/p\u003e\n\u003cp\u003eAlthough the historical period is used as a comparative reference in the analyses, it should be noted that it does not represent a stable or \u0026ldquo;natural\u0026rdquo; climate state. This interval already reflects transformations induced by global warming observed in recent decades, marked by increased frequency of droughts and heatwaves in the Pantanal. Marengo et al. (2021) emphasize that these processes have pushed the biome toward a condition of growing vulnerability, approaching ecological resilience thresholds. Warming acts synergistically, intensifying water deficits, increasing vegetation flammability, and compromising biological cycles. Consequently, even the \u0026ldquo;historical\u0026rdquo; climatology should be understood as part of a transition context, in which current extreme conditions already herald the consolidation of a new thermal and hydrological regime in the Pantanal.\u003c/p\u003e\n\u003cp\u003eThis transition to a new climate regime becomes even more evident when contrasting projected future heat extremes with the observed dynamics of cold extremes. Recent climatological analyses of frost events in the Pantanal, for 1981\u0026ndash;2023, indicate a decreasing trend in both frequency and intensity, which have also become progressively drier over recent decades (Ramos et al., 2025). Therefore, the data point to a clear restructuring of the extremes regime: while cold waves, a historical component of the biome\u0026rsquo;s climate variability, are declining, heatwaves are projected not only to increase but to become the dominant event.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n\u003ch2\u003eSpatial and seasonal analysis\u003c/h2\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n\u003ch2\u003eSpatial analysis\u003c/h2\u003e\n\u003cp\u003eThe spatial distribution of temperature anomalies indicates that the increase in the frequency of extreme heat days is generally widespread across the biome, although with notable regional variations.\u003c/p\u003e\n\u003cp\u003eThese regional differences exhibit a strong and distinct seasonal pattern, as illustrated in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e. During the warmer seasons, spring (SON) and summer (DJF), there is a pronounced increase in event frequency, with the highest intensity observed in the northern and central portions of the Pantanal. In spring, this positive anomaly is particularly critical, exceeding 4 events in the northern part of the biome.\u003c/p\u003e\n\u003cp\u003eIn contrast, during autumn (MAM) and, especially, winter (JJA), the model indicates a tendency toward a reduction in the frequency of these events. This decrease is most pronounced in the southern Pantanal and along its eastern edge, where negative anomalies reach approximately \u0026minus;\u0026thinsp;1 event. This spatial contrast suggests the presence of a north\u0026ndash;south dipole pattern, reflecting the coexistence of areas experiencing both intensification and attenuation of extreme heat within the biome.\u003c/p\u003e\n\u003cp\u003eFor the intermediate-emission scenario SSP2-4.5, the seasonal warming trend intensifies while maintaining a similar spatial pattern, as illustrated in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e. In spring (SON), the spatial pattern is most clearly defined, with a pronounced north\u0026ndash;south gradient. The largest increase in event frequency, exceeding 5 units, is concentrated across the northern half of the biome, gradually weakening toward the south. During summer (DJF), a similar, though more subtle, pattern is projected: increases of up to 2 events are almost entirely confined to the northern third of the Pantanal, while the central and southern portions exhibit minimal changes, close to neutral conditions.\u003c/p\u003e\n\u003cp\u003eIn contrast, winter (JJA) is characterized by a negative anomaly, with the greatest intensity located in the central-southern portion and along the eastern edge of the biome, where reductions in events reach \u0026minus;\u0026thinsp;1. On the western edges and in the far north, the anomaly is less pronounced. Finally, autumn (MAM) is notable for its spatial homogeneity, with near-neutral conditions prevailing across almost the entire floodplain, except for a very minor localized increase in the far northern Pantanal.\u003c/p\u003e\n\u003cp\u003eUnder the high-emission scenario SSP5-8.5, projections indicate a pronounced exacerbation of heat anomalies, with an increase in the frequency of extreme events throughout the year (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e). The attenuation trend observed during autumn and winter in the previous scenarios is completely reversed, and all seasons now exhibit positive anomalies, highlighting persistent thermal stress across the biome.\u003c/p\u003e\n\u003cp\u003eSpring (SON) and autumn (MAM) emerge as the most critical periods, with projected increases of up to 7 and 4 events, respectively. Spatially, the largest changes are consistently concentrated in the northern and central portions of the Pantanal, establishing these areas as future warming hotspots. Even winter (JJA) shows an increase in event frequency, indicating the near elimination of the biome\u0026rsquo;s milder seasonal conditions under this extreme-emission scenario.\u003c/p\u003e\n\u003cp\u003eThe Pantanal biome does not constitute a homogeneous landscape; rather, it is a complex mosaic of distinct sub-regions, each with particularities in flood regime, topography, and vegetation structure. The classification by Silva \u0026amp; Abdon (1998), which delineates 11 sub-regions, is the most widely recognized reference for understanding this internal diversity. When future scenarios are examined through this regional lens, these sub-regions can be grouped into distinct climate-response zones, allowing for a deeper understanding of the biome\u0026rsquo;s vulnerability.\u003c/p\u003e\n\u003cp\u003eThe spatial distribution of anomalies projects a future of uneven warming. In the far northern portion of the biome, the sub-regions of C\u0026aacute;ceres, Pocon\u0026eacute;, and Bar\u0026atilde;o de Melga\u0026ccedil;o form a clear hotspot, expected to experience the most consistent and severe increases in the frequency of heat events. Immediately to the south, the vast central plains of Paiagu\u0026aacute;s and Nhecol\u0026acirc;ndia display a similarly high-impact pattern. Adjacent to these, the western river corridor encompassing the sub-regions of Paraguai, Abobral, Aquidauana, and Miranda also projects significant increases, albeit generally of slightly lower magnitude, functioning as a major transition zone. Finally, a distinct pattern emerges in the southwest, where Porto Murtinho and Nabileque consolidate as the areas of lowest overall warming; here, the phenomenon of seasonal attenuation, with reductions in winter heat events, is most pronounced under low-emission scenarios.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n\u003ch2\u003eSeasonal analysis\u003c/h2\u003e\n\u003cp\u003eEven more revealing than the annual increase is the profound reconfiguration of extreme heat seasonality. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e summarizes the distribution of days in heatwaves across the seasons, highlighting a shift in the annual climate regime.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003e, Average number of days per year in heatwaves (fixed threshold), aggregated by season.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eScenario\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSummer\u003c/p\u003e\n\u003cp\u003e(DJF)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAutumn\u003c/p\u003e\n\u003cp\u003e(MAM)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eWinter\u003c/p\u003e\n\u003cp\u003e(JJA)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSpring\u003c/p\u003e\n\u003cp\u003e(SON)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTotal\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8.20\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSSP1-1.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10.46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e11.30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e23.30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e46.55\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSSP2-4.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12.41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e25.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e48.96\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSSP5-8.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e14.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e18.35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.92\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e31.39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e67.81\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eIn summary, the results indicate a future in which the climate of the Pantanal is not only warmer in its annual mean but is fundamentally restructured (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003e). The projected change creates a significantly longer and more intense \u0026ldquo;heat season,\u0026rdquo; which begins with greater intensity in spring and extends through autumn, drastically altering the thermal regime to which the region\u0026rsquo;s biodiversity and human activities are currently adapted.\u003c/p\u003e\n\u003cp\u003eThe analysis of Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e and Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e reveals three key findings:\u003c/p\u003e\n\u003cp\u003e1. Spring (SON) as the Epicenter of Change: The austral spring emerges as the new epicenter of extreme heat. The number of heatwave days during this season rises sharply from just 2.5 to over 31 under the SSP5-8.5 scenario, representing more than a tenfold increase. This transforms a season that formerly marked the transition into warmth into a period of prolonged and intense thermal stress.\u003c/p\u003e\n\u003cp\u003e2. Expansion of the \u0026ldquo;Hot Season\u0026rdquo; into Autumn (MAM): Autumn, historically characterized by few extreme heat days, becomes a second critical season. With an increase of up to nearly sevenfold (from 2.7 to 18.4 days under SSP5-8.5), the \u0026ldquo;hot season\u0026rdquo; effectively extends, reducing the window of milder temperatures.\u003c/p\u003e\n\u003cp\u003e3. Disruption of the Winter Pattern (JJA): Although winter remains the season with the lowest activity, the SSP5-8.5 scenario already indicates a notable rise (from 1.6 to nearly 4 days). This signal, even if still moderate, is significant, as it shows that under high emissions, not even the coldest season will be immune to the occurrence of positive-temperature extremes.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eClimate change refers to significant and long-term alterations in the statistical properties of the climate system, including its mean state and variability (IPCC, 2021). Empirical evidence indicates that the Earth's average surface temperature has increased by approximately 0.71\u0026deg;C over the past century (Arfasa et al., 2024), primarily due to anthropogenic greenhouse gas emissions. According to the IPCC, global warming of between 1.5\u0026deg;C and 5.8\u0026deg;C is projected throughout the 21st century, increasing the frequency and intensity of extreme events such as heatwaves, floods, and droughts (Wu et al., 2016). These global tendencies directly influence regional hydroclimatic systems, particularly in sensitive biomes such as the Pantanal, where temperature and precipitation changes are expected to drive substantial ecological and hydrological transformations.\u003c/p\u003e\u003cp\u003eRecent hydroclimatic analyses have provided new insights into the global\u0026ndash;regional coupling mechanisms that regulate precipitation variability in the Upper Paraguay River Basin and the Pantanal. Thielen et al. (2020) demonstrated that sea surface temperature (SST) anomalies in the North Atlantic and North Pacific Oceans explain up to 80% of the interannual variance in Pantanal precipitation, with a two-month lag between oceanic warming and rainfall response. This teleconnection pattern indicates that persistent SST warming in the Northern Hemisphere amplifies the likelihood of severe and prolonged droughts, while certain regions of the Southern Hemisphere (South Atlantic, South Pacific, and Indian Oceans) show positive correlations with enhanced precipitation.\u003c/p\u003e\u003cp\u003eThese results emphasize that the Pantanal\u0026rsquo;s hydroclimatic variability is strongly influenced by remote ocean\u0026ndash;atmosphere interactions, which modulate the alternation between flood and drought phases. Consequently, under current global warming trends, the intensification of SST anomalies may lead to more frequent and extreme hydroclimatic oscillations, exacerbating the biome\u0026rsquo;s exposure to compound drought\u0026ndash;heat events already identified in recent years.\u003c/p\u003e\u003cp\u003eThe results of this study indicate an unprecedented intensification of heatwaves in the Pantanal between 2030 and 2060, revealing an increase of 126% to 181% in the number of days exceeding the extreme heat threshold, along with a significant expansion of the \u0026ldquo;hot and dry season.\u0026rdquo; These findings, derived from a fixed threshold anchored in the 1991\u0026ndash;2020 climatological normal, demonstrate that events currently considered rare are likely to become common conditions in the near future. This behavior confirms that the biome is moving toward a new thermal regime in which the frequency and duration of heatwaves will no longer be exceptions but will instead structure the regional climatic seasonality.\u003c/p\u003e\u003cp\u003eThis result converges with recent evidence showing that the Pantanal has already been undergoing, since 2019, a climatic transition characterized by compound drought and heat events (Costa et al., 2024; Libonati et al., 2022; Marengo et al., 2021). The 2019\u0026ndash;2021 period marked a turning point when multi-year droughts and heatwaves acted synergistically, amplifying fire risk and leading to severe environmental losses. In 2020, maximum temperatures exceeded the climatological mean by approximately 6\u0026deg;C during the peaks of these compound events, resulting in unprecedented thermal and hydrological stress (Costa et al., 2024). This land\u0026ndash;atmosphere coupling, in which dry vegetation reinforces warming and vice versa, provides the physical background that explains both the record-breaking fires and the projected pattern of future intensification.\u003c/p\u003e\u003cp\u003eFrom a dynamic perspective, the projection of concentrated heatwave events during spring and autumn confirms the reorganization of thermal seasonality observed in recent data. Miranda et al. (2025) identified that the September\u0026ndash;November (SON-2020) quarter represented the peak of thermal and hydric stress, with a marked reduction in the evaporative fraction and an increase in surface temperature amplitude. These energy balance diagnostics support the hypothesis that the Pantanal climate system is shifting toward a regime dominated by greater heat persistence and reduced hydrological buffering, conditions that favor limited evapotranspiration and energy accumulation at the surface. Consequently, the projected pattern of longer and more frequent heatwaves during seasonal transitions reflects a redistribution of the regional energy balance, with direct implications for ecological and hydrological cycles.\u003c/p\u003e\u003cp\u003eAlthough fire is a recurrent element in parts of the Pantanal\u0026rsquo;s grassland and savanna formations, its behavior and impact are strongly modulated by the hydrological regime of the floodplain. The annual flood pulse acts as a natural limiter to fire spread during the wet season and conditions vegetation recovery in the subsequent rainy period by restoring soil moisture and promoting regrowth. Conversely, years of prolonged drought and low river levels weaken this buffering effect, allowing fires to reach areas usually protected, such as forested patches and gallery forests, and increasing both the severity and extent of burns (Garcia et al., 2021; Marengo et al., 2021; Pelissari et al., 2023). In 2020, for instance, annual rainfall was approximately 26% below the 1982\u0026ndash;2020 average, and the number of active fire detections increased by 123% compared to the 2002\u0026ndash;2020 mean. Fires affected roughly one-third of the Pantanal's total area (~\u0026thinsp;40,000 km\u0026sup2;), including about 28% of wetlands that dried out due to drought conditions (Mataveli et al., 2021). These observations provide quantitative evidence of how hydrological disruption and thermal intensification interact to amplify fire activity across the floodplain.\u003c/p\u003e\u003cp\u003eThe multiscale analysis by Costa et al. (2024) showed that the 2019\u0026ndash;2021 heatwave and drought covered up to 89% of the Pantanal on a 12-month scale, reinforcing that the combined effects are more relevant than isolated ones. Similarly, Ribeiro et al. (2022) demonstrated that the co-occurrence of high atmospheric dryness (elevated VPD) and low precipitation was the main driver of fires in both the Pantanal and Xingu, with a historical 5\u0026ndash;10% increase in fire risk associated with compound events. These results suggest that, under global warming, the probability of \u0026ldquo;hot and dry\u0026rdquo; states will substantially increase, even under mitigation scenarios, aligning with the projections presented here. Thus, the future increments in WSDI and HWFI projected in this study are not merely statistical artifacts but represent a tangible physical risk of intensifying coupling between heat, dryness, and flammability.\u003c/p\u003e\u003cp\u003eThe year 2020, considered the most severe environmental disaster ever recorded in the Pantanal (Garcia et al., 2021; Pelissari et al., 2023), provides an empirical analogy of what the future may resemble under SSP2-4.5 and SSP5-8.5 scenarios. Pelissari et al. (2023) reported 300,127 fire hotspots between 2001 and 2022, with 2020 showing the lowest precipitation and the highest fire occurrence and burn severity (high ΔNBR). Together, these analyses reveal that the 2019\u0026ndash;2021 megadrought and heatwaves have already anticipated the mean conditions projected for 2030\u0026ndash;2060. The transition detected in this study, from an annual average of 36 to up to 103 days above the 90th percentile temperature, therefore represents the consolidation of a regime already empirically underway.\u003c/p\u003e\u003cp\u003eThe ecological implications of this transformation are profound. In savanna and grassland ecosystems, fire plays a key ecological role in nutrient cycling and maintaining open vegetation structure, but altered intensity and frequency may exceed the adaptive tolerance of species (Valente \u0026amp; Laurini, 2024; Pivello et al., 2021). Analyses of burn severity (Pelissari et al., 2023) and productivity (GPP) indicate that vegetation resilience decreases drastically under hot and dry events, with heterogeneous recovery and phenological delays. Miranda et al. (2025) also reported a marked decline in evaporative fraction and CO₂ flux during 2018\u0026ndash;2020, reinforcing the physiological impact of heat\u0026ndash;drought interactions. Therefore, the projected increase in heatwaves is expected to intensify plant thermal and hydric stress, with direct implications for ecosystem flammability and productivity.\u003c/p\u003e\u003cp\u003eFrom a biological standpoint, the effects extend beyond vegetation. The 2020 thermal collapse resulted in an estimated death of 17\u0026nbsp;million vertebrates, including endemic species (Tomas et al., 2021). Magioli et al. (2024) showed that even one year after the megafire, there was a decline in mammal richness and abundance in monospecific forests, evidencing that the recurrence of severe events may erode regional beta diversity. In the projected scenario, where heatwaves become seasonal and prolonged, Pantanal fauna will face direct thermal stress, habitat loss, and fragmentation of hydric refugia. Therefore, the projected thermal intensification represents not merely a climatic issue but a direct threat to the functional integrity of the biome\u0026rsquo;s biodiversity.\u003c/p\u003e\u003cp\u003eThe expansion of the \u0026ldquo;hot and dry season,\u0026rdquo; beginning in August and extending until April, also has critical implications for fire management and environmental planning. Recent studies by Couto et al. (2024, 2025) demonstrated that convective gusts and gust fronts during extremely hot and dry days can multiply fire propagation rates, especially when fine fuels are desiccated. The projected trend of more persistent heat implies greater overlap between periods of meteorological fire risk and the availability of flammable fuel, requiring structural changes in prescribed burning windows, brigade positioning, and monitoring calendars. The recommendation by Garcia et al. (2021) for a permanent Integrated Fire Management (MIF) program becomes even more urgent in light of the projections presented here.\u003c/p\u003e\u003cp\u003eSpatial analysis in this study also indicates that the northern and central portions of the Pantanal (subregions of C\u0026aacute;ceres, Pocon\u0026eacute;, Bar\u0026atilde;o de Melga\u0026ccedil;o, Paiagu\u0026aacute;s, and Nhecol\u0026acirc;ndia) will be the most affected by the increase in heatwaves, coinciding with areas that historically show the highest fire severity (Pelissari et al., 2023; Valente \u0026amp; Laurini, 2024). This pattern reinforces the need for regionally differentiated management and conservation strategies that integrate thermal risk indicators (WSDI, HWFI) with hydrological and vegetation metrics.\u003c/p\u003e\u003cp\u003eAltogether, the results point to three fundamental mechanisms driving the Pantanal\u0026rsquo;s transition toward a new climatic regime: (1) a positive feedback between vegetation desiccation and surface warming, (2) the weakening of seasonal hydrological buffering that traditionally mitigated heat extremes, and (3) the extension of the meteorological fire-risk period into what was formerly the wet season. This triad defines the structural shift that underpins the observed and projected intensification of compound hot\u0026ndash;dry conditions across the biome.\u003c/p\u003e\u003cp\u003eFinally, the results confirm the central hypothesis that the Pantanal is moving toward a new climatic regime in which extreme heat will not be an episodic event but a structuring condition. Even under the most optimistic scenario (SSP1-1.9), projections indicate that nearly one-quarter of the year will occur under extreme heat conditions, profoundly transforming ecological cycles, agricultural practices, and the socio-environmental security of the biome. The evidence converges on the urgent need for mitigation and adaptation policies that combine high-resolution climate monitoring, the strengthening of local firefighting brigades, and the integration of scientific knowledge with traditional wisdom in territorial planning for the Pantanal.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThese findings illustrate that the Pantanal\u0026rsquo;s transition toward a new heat-dominated regime represents a paradigmatic case of regional environmental change. The intensification of heatwaves and the expansion of the dry\u0026ndash;hot season point to systemic feedbacks between climate, hydrology, and fire regimes. This has profound implications for land-use planning, conservation policy, and adaptive fire management, emphasizing the need for regionally tailored climate resilience strategies\u003c/p\u003e\u003cp\u003eThe reference climatology (1991\u0026ndash;2020) already reflects a world profoundly altered by climate change, with intensified thermal extremes compared to the pre-industrial period. Even the so-called \u0026ldquo;baseline climate\u0026rdquo; does not represent stable or natural conditions, but an already transformed state of the global climate system.\u003c/p\u003e\u003cp\u003eAmong the evaluated models, the Japanese MRI-ESM2-0 showed the best performance in reproducing daily maximum temperatures over the Pantanal. Projections based on this altered baseline indicate a pronounced intensification of heatwaves under future climate scenarios (2030\u0026ndash;2060), with an increase of 126% to 181% in extreme heat days and a marked expansion of the \u0026ldquo;hot and dry season.\u0026rdquo; Conditions currently considered exceptional will become common, fundamentally restructuring the region\u0026rsquo;s thermal regime.\u003c/p\u003e\u003cp\u003eThe austral spring emerges as the new epicenter of extreme heat, while autumn experiences a notable extension of high-temperature conditions. Even winter, historically the mildest season, is projected to show substantial increases in extreme events under high-emission scenarios. Spatial analysis reveals that northern and central subregions will experience the most severe and persistent heat, highlighting the heterogeneous nature of climate impacts across the biome.\u003c/p\u003e\u003cp\u003eThese projected changes have profound ecological and socio-environmental implications. Intensified heatwaves are likely to exacerbate fire risk, reduce vegetation resilience, disrupt ecological cycles, and threaten biodiversity, including endemic species. Overlaps with existing droughts may further challenge agriculture, water management, and human well-being. The findings underscore the urgent need for integrated mitigation and adaptation strategies, including high-resolution climate monitoring, targeted fire management, and ecosystem-based approaches to safeguard both biodiversity and socio-environmental security. Beyond climatic implications, these results reveal a profound bioclimatic restructuring of the Pantanal, with potential impacts on species adaptation, ecosystem resilience, and fire regimes.\u003c/p\u003e\u003cp\u003eIn summary, the Pantanal is entering a new climatic regime in which extreme heat is not merely episodic but a structuring feature of the regional climate. These changes expose the biome to compounded risks from heat, drought, and fires, emphasizing the critical need for proactive measures to preserve ecosystem resilience and regional sustainability.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis work was supported by [nome da agência de fomento] (Grant number: [número do projeto]).\u003c/p\u003e\n\u003cp\u003eCompeting Interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no relevant financial or non-financial interests related to this work.\u003c/p\u003e\n\u003cp\u003eAuthor Contributions\u003c/p\u003e\n\u003cp\u003eThe manuscript was conceived and developed by [Autor A]. All other authors reviewed the manuscript and contributed to the writing of the final version.\u003c/p\u003e\n\u003cp\u003eData Availability\u003c/p\u003e\n\u003cp\u003eThe data supporting the findings of this study are available from the authors upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eALHO CJR, Sabino J (2012) Seasonal Pantanal flood pulse: implications for biodiversity conservation, a review. \u003cem\u003eOecologia Australis\u003c/em\u003e 16(4):958\u0026ndash;978\u003c/li\u003e\n\u003cli\u003eARFASA E M, TESFAYE B, GETAHUN M, ALEMU A D, BEKELE M (2024) Climate change and variability impacts on hydroclimatic resources: a global synthesis. \u003cem\u003eEnviron Earth Sci\u003c/em\u003e **83:**1\u0026ndash;15. https://doi.org/10.1007/s12665-024-11829-3\u003c/li\u003e\n\u003cli\u003eCOSTA MC, Marengo JA, Alves LM, Cunha AP (2024) Multiscale analysis of drought, heatwaves, and compound events in the Brazilian Pantanal in 2019\u0026ndash;2021. \u003cem\u003eTheoretical and Applied Climatology\u003c/em\u003e 155:661\u0026ndash;677. https://doi.org/10.1007/s00704-023-04731-1\u003c/li\u003e\n\u003cli\u003eCOUTO FT et al (2024) A case study of the possible meteorological causes of unexpected fire behavior in the Pantanal wetland, Brazil. \u003cem\u003eEarth\u003c/em\u003e 5(3):548\u0026ndash;563. https://doi.org/10.3390/earth5030032\u003c/li\u003e\n\u003cli\u003eCOUTO FT et al (2025) Exploratory analysis of atmospheric modelling use over Pantanal wildfires. \u003cem\u003eRA\u0026rsquo;EGA \u0026ndash; O Espa\u0026ccedil;o Geogr\u0026aacute;fico em An\u0026aacute;lise\u003c/em\u003e 63(2):35\u0026ndash;56\u003c/li\u003e\n\u003cli\u003eFERON S, Cordero RR, Damiani A, MacDonell S, Pizarro J, Goubanova K, Valenzuela R, Wang C, Rester L, Beaulieu A (2024) South America is becoming warmer, drier, and more flammable. \u003cem\u003eCommunications Earth \u0026amp; Environment\u003c/em\u003e 5:501. https://doi.org/10.1038/s43247-024-01289-y\u003c/li\u003e\n\u003cli\u003eGARCIA LC et al (2021) Record-breaking wildfires in the world\u0026rsquo;s largest continuous tropical wetland: integrative fire management is urgently needed for both biodiversity and humans. \u003cem\u003eJournal of Environmental Management\u003c/em\u003e 293:112870. https://doi.org/10.1016/j.jenvman.2021.112870\u003c/li\u003e\n\u003cli\u003eHAMILTON SK (2002) Hydrological controls of ecological structure and function in the Pantanal wetland (Brazil). In: McClain ME (ed) \u003cem\u003eThe Ecohydrology of South American Rivers and Wetlands\u003c/em\u003e. IAHS Special Publication 6:133\u0026ndash;158\u003c/li\u003e\n\u003cli\u003eIPCC (2021) \u003cem\u003eClimate Change 2021: The Physical Science Basis.\u003c/em\u003e Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge\u003c/li\u003e\n\u003cli\u003eLIBONATI R, Dacamara CC, Peres LF, Sander de Carvalho LA, Garcia LC (2020) Rescue Brazil\u0026rsquo;s burning Pantanal wetlands. \u003cem\u003eNature\u003c/em\u003e 588:217\u0026ndash;219. https://doi.org/10.1038/d41586-020-03441-7\u003c/li\u003e\n\u003cli\u003eLIBONATI R et al (2022) Assessing the role of compound drought and heatwave events on unprecedented 2020 wildfires in the Pantanal. \u003cem\u003eEnvironmental Research Letters\u003c/em\u003e 17(1):015005. https://doi.org/10.1088/1748-9326/ac3a5a\u003c/li\u003e\n\u003cli\u003eMATAVELI G A V, Pereira G, de Oliveira G, Seixas H T, Cardozo F S, Shimabukuro Y E, Kawakubo F S, Brunsell N A (2021) 2020 Pantanal\u0026rsquo;s widespread fire: short- and long-term implications for biodiversity and conservation. \u003cem\u003eBiodivers Conserv\u003c/em\u003e **30:**3299\u0026ndash;3303. https://doi.org/10.1007/s10531-021-02243-2\u003c/li\u003e\n\u003cli\u003eMAGIOLI M et al (2024) Forest type modulates mammalian responses to megafires. \u003cem\u003eScientific Reports\u003c/em\u003e 14:13538. https://doi.org/10.1038/s41598-024-61823-8\u003c/li\u003e\n\u003cli\u003eMARENGO JA et al (2021) Extreme drought in the Brazilian Pantanal in 2019\u0026ndash;2020: characterization, causes, and impacts. \u003cem\u003eFrontiers in Water\u003c/em\u003e 3:639204. https://doi.org/10.3389/frwa.2021.639204\u003c/li\u003e\n\u003cli\u003eMIRANDA V et al (2025) Evapotranspiration anomalies over the Pantanal region during the droughts 2018\u0026ndash;2020. \u003cem\u003eRecent Advances in Remote Sensing\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eNETO JBF, Carpenedo CB, Pereira G (2026) Ondas de calor e de frio em clima tropical de altitude: evid\u0026ecirc;ncias entre 1994 e 2060. \u003cem\u003eRevista Territorium Terram\u003c/em\u003e 9(Special Issue 1)\u003c/li\u003e\n\u003cli\u003ePELISSARI TD et al (2023) Dynamics of major environmental disasters involving fire in the Brazilian Pantanal. \u003cem\u003eScientific Reports\u003c/em\u003e 13:21669. https://doi.org/10.1038/s41598-023-49074-4\u003c/li\u003e\n\u003cli\u003ePIVELLO VR et al (2021) Understanding Brazil\u0026rsquo;s catastrophic fires: causes, consequences and policy needed to prevent future tragedies. \u003cem\u003ePerspectives in Ecology and Conservation\u003c/em\u003e 19:233\u0026ndash;255. https://doi.org/10.1016/j.pecon.2021.06.002\u003c/li\u003e\n\u003cli\u003ePLETSCH MAJS, Silva Junior CHL, Penha TV, K\u0026ouml;rting TS, Silva MES, Pereira G, Anderson LO, Arag\u0026atilde;o LEOC (2021) The 2020 Brazilian Pantanal fires. \u003cem\u003eAnais da Academia Brasileira de Ci\u0026ecirc;ncias\u003c/em\u003e 93(3):e20210077. https://doi.org/10.1590/0001-3765202120210077\u003c/li\u003e\n\u003cli\u003eRAMOS RC, Pereira G, Galvani E, Cardozo FS, Santos PR, Dutra SB (2025) Din\u0026acirc;mica dos eventos de friagem na regi\u0026atilde;o do Pantanal: um estudo com dados de rean\u0026aacute;lise de 1981 a 2023. \u003cem\u003eRevista Brasileira de Climatologia\u003c/em\u003e 37(21):138\u0026ndash;162. https://doi.org/10.55761/abclima.v37i21.19967\u003c/li\u003e\n\u003cli\u003eREBOITA MS, Ferreira GWS, Ribeiro JGM, Ali S (2024) Assessment of precipitation and near-surface temperature simulation by CMIP6 models in South America. \u003cem\u003eEnvironmental Research: Climate\u003c/em\u003e 3(2):025011. https://doi.org/10.1088/2752-5295/ad3b4b\u003c/li\u003e\n\u003cli\u003eRIBEIRO AFS et al (2022) A compound event-oriented framework to tropical fire risk assessment in a changing climate. \u003cem\u003eEnvironmental Research Letters\u003c/em\u003e 17(6):065015. https://doi.org/10.1088/1748-9326/ac6da5\u003c/li\u003e\n\u003cli\u003eSANTOS DM et al (2024) Compound dry-hot-fire events connecting Central and Southeastern South America: an unapparent and deadly ripple effect. \u003cem\u003enpj Natural Hazards\u003c/em\u003e 1:32. https://doi.org/10.1038/s44298-024-00033-8\u003c/li\u003e\n\u003cli\u003eSCH\u0026Auml;R C et al (2004) The role of increasing temperature variability in European summer heatwaves. \u003cem\u003eNature\u003c/em\u003e 427:332\u0026ndash;336. https://doi.org/10.1038/nature02300\u003c/li\u003e\n\u003cli\u003eSENEVIRATNE SI et al (2021) Weather and climate extreme events in a changing climate. In: \u003cem\u003eClimate Change 2021: The Physical Science Basis.\u003c/em\u003e Cambridge University Press, Cambridge, pp 1513\u0026ndash;1766\u003c/li\u003e\n\u003cli\u003eSHIMABUKURO YE, de Oliveira G, Pereira G, Arai E, Cardozo F, Dutra AC, Mataveli G (2023) Assessment of burned areas during the Pantanal fire crisis in 2020 using Sentinel-2 images. \u003cem\u003eFire\u003c/em\u003e 6(7):277. https://doi.org/10.3390/fire6070277\u003c/li\u003e\n\u003cli\u003eSILVA PSV et al (2024) Joining forces to fight wildfires: science and management in a protected area of Pantanal, Brazil. \u003cem\u003eEnvironmental Science and Policy\u003c/em\u003e 159:103818. https://doi.org/10.1016/j.envsci.2024.103818\u003c/li\u003e\n\u003cli\u003eSILVA JSV, Abdon MM (1998) Delimita\u0026ccedil;\u0026atilde;o do Pantanal brasileiro e suas sub-regi\u0026otilde;es. \u003cem\u003ePesquisa Agropecu\u0026aacute;ria Brasileira\u003c/em\u003e 33(Suppl):1703\u0026ndash;1711\u003c/li\u003e\n\u003cli\u003eTHIELEN, D., SCHUCHMANN, K.-L., RAMONI-PERAZZI, P., M\u0026Aacute;RQUEZ, M., ROJAS, W., QUINTERO, J. I., \u0026amp; MARQUES, M. I. M. (2020). Quo vadis Pantanal? Expected precipitation extremes and drought dynamics from changing sea surface temperature. PLOS ONE, 15(1), e0227437. https://doi.org/10.1371/journal.pone.0227437)\u003c/li\u003e\n\u003cli\u003eTHOMAS NP, Marquardt Collow AB, Bosilovich MG, Dezfuli A (2023) Effect of baseline period on quantification of climate extremes over the United States. \u003cem\u003eGeophysical Research Letters\u003c/em\u003e 50:e2023GL105204. https://doi.org/10.1029/2023GL105204\u003c/li\u003e\n\u003cli\u003eTOMAS WM et al (2019) Sustainability agenda for the Pantanal wetland: perspectives on a collaborative interface for science, policy, and decision-making. \u003cem\u003eTropical Conservation Science\u003c/em\u003e 12:1\u0026ndash;30. https://doi.org/10.1177/1940082919872634\u003c/li\u003e\n\u003cli\u003eTOMAS WM et al (2021) Distance sampling surveys reveal 17 million vertebrates directly killed by the 2020's wildfires in the Pantanal, Brazil. \u003cem\u003eScientific Reports\u003c/em\u003e 11:23547. https://doi.org/10.1038/s41598-021-02611-0\u003c/li\u003e\n\u003cli\u003eVALENTE F, Laurini M (2024) The dynamics of fire activity in the Brazilian Pantanal: a log-Gaussian Cox process-based structural decomposition. \u003cem\u003eFire\u003c/em\u003e 7(5):170. https://doi.org/10.3390/fire7050170\u003c/li\u003e\n\u003cli\u003eWU T, ZHANG J, LI W, LIU Y, LI L (2016) Global climate projections under different emission scenarios: summary for policymakers. \u003cem\u003eClim Dyn\u003c/em\u003e **47:**321\u0026ndash;336. https://doi.org/10.1007/s00382-016-2907-8\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bulletin-of-atmospheric-science-and-technology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bast","sideBox":"Learn more about [Bulletin of Atmospheric Science and Technology](http://www.springer.com/journal/42865)","snPcode":"42865","submissionUrl":"https://submission.nature.com/new-submission/42865/3","title":"Bulletin of Atmospheric Science and Technology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"WSDI, HWFI, CAMS-CSM1-0, EC-Earth3-Veg, GFDL-ESM4","lastPublishedDoi":"10.21203/rs.3.rs-8119100/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8119100/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Pantanal, the world's largest continuous floodplain, exhibits high sensitivity to thermal and hydrological variations, making it particularly vulnerable to the combined effects of climate change and anthropogenic pressures. The simultaneous occurrence of droughts and heatwaves intensifies vegetation flammability and has been a determining factor in the amplification of fires. Given this scenario, this study aimed to quantify and characterize future changes in the frequency, duration, and seasonality of heatwaves in the Pantanal for the period 2030\u0026ndash;2060, analyzing projections under the SSP1-1.9, SSP2-4.5, and SSP5-8.5 emission scenarios, compared to the climatological normal of 1991\u0026ndash;2020. For this purpose, four CMIP6 models were validated against the ERA5 reanalysis, with the MRI-ESM2-0 model selected for its superior performance in representing the daily maximum temperature in the biome area. The results show a significant increase in the occurrence of these events in all scenarios, with an average increase of +\u0026thinsp;126% to +\u0026thinsp;181% in the number of days above the extreme heat threshold. Heatwaves become more prolonged and concentrated in spring and autumn, indicating a restructuring of the biome's thermal seasonality. These findings suggest that the Pantanal is moving towards a new climate regime, characterized by intensified thermal stress and the expansion of the \u0026ldquo;hot and dry season,\u0026rdquo; which will have direct implications for biodiversity and fire management.\u003c/p\u003e","manuscriptTitle":"Towards a new climate regime: heatwaves proliferate and reshape seasonality in the world's largest tropical wetland","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-02 14:09:01","doi":"10.21203/rs.3.rs-8119100/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-27T14:30:20+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-27T12:36:29+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-23T15:32:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"195314322706496156788670152611995365074","date":"2025-12-01T18:37:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"32817169194397818858044495710024438655","date":"2025-11-27T10:13:08+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-27T07:17:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-22T00:58:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-21T05:49:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"Bulletin of Atmospheric Science and Technology","date":"2025-11-15T03:24:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bulletin-of-atmospheric-science-and-technology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bast","sideBox":"Learn more about [Bulletin of Atmospheric Science and Technology](http://www.springer.com/journal/42865)","snPcode":"42865","submissionUrl":"https://submission.nature.com/new-submission/42865/3","title":"Bulletin of Atmospheric Science and Technology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"2f7f37dc-e86b-4fe2-81ad-f7131dfb457c","owner":[],"postedDate":"December 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-02T16:01:24+00:00","versionOfRecord":{"articleIdentity":"rs-8119100","link":"https://doi.org/10.1007/s42865-026-00122-8","journal":{"identity":"bulletin-of-atmospheric-science-and-technology","isVorOnly":false,"title":"Bulletin of Atmospheric Science and Technology"},"publishedOn":"2026-02-23 15:58:09","publishedOnDateReadable":"February 23rd, 2026"},"versionCreatedAt":"2025-12-02 14:09:01","video":"","vorDoi":"10.1007/s42865-026-00122-8","vorDoiUrl":"https://doi.org/10.1007/s42865-026-00122-8","workflowStages":[]},"version":"v1","identity":"rs-8119100","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8119100","identity":"rs-8119100","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}