The El Niño Southern Oscillation (ENSO) is a key driver of global climate variability. Through its teleconnections, ENSO influences precipitation and temperature worldwide, yet its influence on compound extremes across agricultural systems is less well explored. Here, we examine ENSO’s influence on co-occurring hot-dry (HD) and hot-wet (HW) extreme events across global croplands, given their potential risks for food production. HD events can intensify crop water stress, while HW events can increase risks of pests, pathogens, and post-harvest losses. Using the Niño 3.4 index, high-resolution climate data (1901–2024), and crop extent and calendar data, we quantify the localized risk of these compound heat events and assess exposure for all croplands as well as staple crops of maize, rice, soybeans, and wheat. We show increased risk of both HD and HW events across global croplands during El Niño years. For example, the localized risk of HD events more than doubles across parts of global croplands including India, Australia, the Sahel, Brazil and Mexico during El Niño relative to ENSO-Neutral years, resulting in a significantly elevated global cropland HD exposure during developing El Niño summers. Localized risk of HW events also more than doubles particularly across India, Argentina, Brazil, and parts of Sahel with significantly elevated cropland exposure in decaying El Niño summers. Of the four staple crops, rice shows the strongest link to ENSO, with global exposure increasing >30% and >40% above Neutral years for HD and HW, respectively. These findings demonstrate that ENSO is associated with shifts in multi-crop exposure to compound heat extremes, highlighting the need to integrate ENSO-conditioned compound event risks into early warning systems and country- and crop-specific adaptation strategies to safeguard climate-related agricultural risks.

Globally, fires in 2025 burned the second-lowest area on record since 2002 and emitted the third-lowest CO2 total. Yet, a third successive year of extreme wildfire emissions prevailed in Canada, and catastrophic fires in Los Angeles, South Korea and Europe killed over 90 people and forced over 300,000 evacuations.

Extreme fire weather (hot, dry, and windy conditions) has intensified globally, yet formally attributing this trend to anthropogenic climate change remains challenging. Here, we analyze global trends in extreme fire weather days (FWI95d, annual count of days with Fire Weather Index above the 95th percentile) over 1980-2023, using climate model ensembles, observational data, and fingerprint detection techniques. We find that the observed increase in extreme fire weather bears a clear externally forced signal, detectable at 99% confidence above natural variability and attributable to human-induced climate change. This emerging human-induced fingerprint on extreme fire weather highlights a benchmark for climate science and underscores the urgency of integrating these insights into wildfire risk management and adaptation strategies.

Abstract Tropical cyclones (TCs) are major drivers of contiguous United States (CONUS) flooding, yet their contribution to hourly precipitation extremes remains poorly quantified. Here we link observations from 420 gauges (1980–2024) with TC track data to attribute extreme hourly precipitation to both local and remote TCs. Remote TC contributions are identified via atmospheric river (AR) objects using the TempestExtremes framework applied to a global AR database. We identify 254 TCs that contributed to extreme hourly precipitation, affecting 76% of stations. While local contributions from Atlantic TCs are prevalent in eastern CONUS, remote influences from Pacific TCs contribute to precipitation extremes in the rest of CONUS. The number of TCs resulting in extreme hourly precipitation has increased significantly in northeast and southeast CONUS since 1980. These results reveal that TC‐linked moisture represents a contributor to short‐duration precipitation extremes across a broader area of CONUS than previously recognized.

We define fire weather waves as persistent extreme fire weather, which can intensify fire activity by sustaining exceptionally warm, dry, and windy conditions. Here, we use daily fire weather index, fire activity, and meteorological data to examine the impacts of fire weather waves on fires, as well as their patterns and trends across global terrestrial ecoregions. Fire weather waves account for only 4% of days but coincide with 26% of the area burned and nearly half of the top 1% most energetic fires in forested ecoregions. Compared with grassland and shrubland fires, forest fires exhibit a larger and more persistent increase in daily burned area in response to fire weather waves, particularly in Mediterranean forests. Fire weather wave frequency has significantly increased across most burnable lands during 1979–2024. Climate projections indicate that fire weather waves will increase throughout the 21st century. These findings underscore fire weather waves as an essential component of early warning systems to strengthen preparedness for extreme fires. Fire weather waves are strongly linked to both overall fire activity and the most extreme wildfire events, highlighting their value as a predictor of fire risk, based on an analysis of fire, fire weather index, and meteorological data.

Extreme meteorological events contribute to 80% of agroecosystem loss indemnities. Daily, seasonal, and annual precipitation dynamics also affect plant production, erosion, sediment and nutrient loading, and soil health across agroecosystems, largely depending on precipitation timing, amount, frequency, and intensity. Gridded meteorological data have been publicly available since the mid-2000s and used to assess spatiotemporal precipitation dynamics. The accuracy of gridded compared to site-level precipitation is, however, rarely evaluated because most long-term meteorological data are already calculated into gridded databases. At the Long-Term Agroecosystem Network Texas Gulf site in central Texas, however, a nearly 90-year precipitation record exists from a suite of 15 on-site rain gauges that have not been used in gridded dataset development. The objective of this study was to evaluate the accuracy of historical precipitation acquired from 15 on-site monitoring stations with the common gridded databases of the Parameter-Elevation Regressions on Independent Slopes Model (PRISM), the Daily Surface Weather and Climatological Summaries (DayMET), and the Gridded Surface Meteorological Dataset (GridMET). Our findings suggest precipitation is highly variable spatiotemporally. Annual and seasonal gridded data from all sources were significantly correlated with on-site weather station precipitation, but GridMET produced the strongest correlations with on-site data. Across time, the accuracy of gridded precipitation data improved, especially between 1980 and 2000 decades. Extreme daily precipitation events acquired from gridded data sources, however, were poorly correlated with actual precipitation at rain gauge sites. These results suggest that gridded data can be helpful for long-term management planning but also showcase a limited utility of gridded data for monitoring assessments, especially as they relate extreme precipitation to erosion, crop loss, and insurance indemnities.

Precipitation is a primary driver of plant production across most agroecosystems. As such, enhanced precipitation forecasting will directly benefit agroecosystem management planning and decisions related to stocking rates, crop selection, restoration seedings, and wildfire fuel loads. When precipitation occurs primarily through convective storms, as is the case in the southern Great Plains, precipitation can be extremely dynamic and difficult to forecast. Providing stakeholders accurate estimates of forecast utility would provide them with the information necessary for proactive land management decision-making. In this case study at the Texas Gulf Long-Term Agroecosystem Research site, the objectives were to (1) optimize individual forecast models acquired from National Oceanic and Atmospheric Administration's North American Multi-Model Ensemble and (2) assess the correlation between historical precipitation and retrospectively forecasted (hindcast) precipitation from 1982 to 2024. Optimal forecasts were often obtained from three to five aggregate combinations of individual models that were accurate for most of the year. Seasonal forecasts always produced greater forecast skill than monthly forecasts, and the best forecasts were produced in autumn and spring. Alternatively, when forecasts were acquired in autumn, forecast skill was poorer compared to when forecasts were acquired in early spring, and there were no reliable forecasts produced from 2-month lead times at this site. El Niño Southern Oscillation conditions were, however, accurately accounted for by individual forecast models. Collectively, these findings suggest that there is utility in using precipitation forecasting for proactive agroecosystem management and planning even in dynamic precipitation regions such as the southern Great Plains.

Snow is changing globally. Computationally intensive snow reanalysis products and downscaled climate model projections allow for the estimation of historical and projected changes in snow over ∼4–10 km resolutions, but these resolutions are coarse relative to the scales needed for water supply and flood planning. Fine-scale digital elevation models (DEMs) are widely available but are underutilized to make first-order assessments of snow vulnerability. Here, we leverage DEMs at a 7.5 arc s (∼250 m) resolution, combining these with historical freezing level height estimates from ERA-5 to derive estimates of changes in the snow-receiving area (SRA) and its variability across global mountain ranges. Results show estimated SRA declines in 29% (1.9 million km ^2 ) of the global mountain area from 1982–2020; 66% of the mountainous areas had no change over the historical period. At +1.5 °C of warming relative to the pre-industrial control, global mountain SRA would decline by 9.5% (1.0 million km ^2 ) relative to recent conditions. This loss would be approximately doubled with +2 °C of warming. In a +4 °C warming scenario, an additional 34% (3.6 million km ^2 ) of SRA would be lost beyond the +2 °C case. Across individual mountain ranges, SRA losses can occur nonlinearly with warming, with some locations that have historically had relatively minor SRA losses at risk of substantially larger losses in warmer climates. Analysis using coarser-resolution DEMs can underestimate or overestimate SRA and its rate of loss, with the largest impacts in relatively warm, low-elevation mountain ranges. Results of this work provide estimates of projected loss in SRA at policy-relevant warming levels; inform the resolutions needed for process-based snow modeling; identify snow vulnerability hotspots; and provide a new integrated approach to snow vulnerability assessment that is achievable at global scales and highlights potential nonlinearities from recent trends to a variety of future warming scenarios.

Concurrent extreme fire weather creates favorable conditions for widespread large fires, which can complicate the coordination of fire suppression resources and degrade regional air quality. Here, we examine the patterns and trends of intra- and interregional synchronous fire weather (SFW) and explore their links to climate variability and air quality impacts. We find climatologically elevated intraregional SFW in boreal regions, as well as interregional synchronicity among northern temperate and boreal regions. Significant increases in SFW occurred during 1979 to 2024, with more than a twofold increase observed in most regions. We estimate that over half of the observed increase is attributable to anthropogenic climate change. Internal modes of climate variability strongly influence SFW in several regions, including Equatorial Asia, which experiences 43 additional intraregional SFW days during El Niño years. Furthermore, SFW is strongly correlated with regional fire-sourced PM 2.5 in multiple regions globally. These findings highlight the growing challenges posed by SFW for firefighting coordination and human health.

Cutoff lows (COL) are upper-level lows that are displaced from the jet stream and are often associated with a stagnant circulation that facilitates prolonged periods of precipitation and subsequent hydrologic hazards. Here, we quantify the contribution of COL to annual and seasonal precipitation totals, extremes, and hydrologic disasters across the globe, as well as explore how precipitation coincident with COL has changed in observations and is projected to change under future scenarios. Many locations received at least 20% of their annual precipitation coincident to COL across portions of the globe, with notable hotspots along the equatorward flank of the jet stream and in rain shadowed areas of the mid-latitudes. Further, regions-including the Mediterranean, southeastern Australia, the interior western United States (US), South Africa, and Northeast China-had over a third of their three-day annual maximum precipitation events coincident with COL. We also show that an estimated 6.4% of total global economic losses from flood-related disasters outside of the equatorial zone were associated with COL during 2000-2023. Significant increases in precipitation concurrent with COL occurred during 1979-2024 in portions of Europe and the northern US and southern Canada with significant declines in eastern Australia. Projections using output from the Community Earth System Model version 2 Large Ensemble show a robust increase in COL precipitation of similar to 20% over the central US and eastern Europe by 2041-2070 tied to a poleward migration of the jet streams. Our results suggest that COL are an important mechanism for generating extreme precipitation and flooding impacts in many global regions, with their contribution to precipitation totals projected to rise in the coming decades in parts of the Northern Hemisphere mid-latitudes.

The February 2024 Las Tablas wildfire in central Chile killed at least 137 civilians, the highest number of wildfire fatalities on record in South America and the most fatalities in a single wildfire event globally since the 2009 Black Saturday fires in Australia. As mass fatality wildfires increase globally, there is an urgent need to understand drivers and effective mitigation strategies to avert future catastrophic loss of life. We characterized the spatiotemporal development of the Valparaíso Las Tablas Fire and identified key biophysical and social factors that contributed to the observed catastrophic outcomes. While the fire behavior was initially more moderate, the offshore wind induced extreme fire behavior as it consumed unmanaged forestry plantations and generated an ember storm that jumped across the heavily vegetated Marga Marga river valley. Topography and dense vegetation fueled a firestorm across multiple neighborhoods simultaneously in the Wildland Urban Interface, including one of the largest informal (unregulated) settlements in the city, where socially vulnerable residents struggled to evacuate to safety. We further identify commonalities with other fatal wildfires globally, including drought, offshore winds, socially vulnerable populations, and inadequate public policy. The Las Tablas Fire showcased the three legs of the wildfire vulnerability triangle, pointing to a critical need to adapt mitigation strategies across regions to avert escalating climate tragedies in the Pyrocene.

Wildfires have increasingly affected human and natural systems across the western United States (WUS) in recent decades. Given that the majority of ignitions are human-caused and potentially preventable, improving the ability to predict fire occurrence is critical for effective wildfire prevention and risk mitigation. We used over 500,000 wildfire ignition records from 2000 to 2020 to develop machine learning models that predict daily ignition probability across the WUS and incorporate a wide range of physical, biological, social, and administrative variables. A key innovation of this work is development of novel sampling techniques for representing ignition absence. Unlike traditional purely random sampling or hyper-sampling, which does not account for temporally autocorrelated factors (such as droughts, insect outbreaks, and heatwaves) and spatially autocorrelated factors (such as proximity to human settlements, infrastructure presence, and fuel type), we introduce spatially and temporally stratified sampling of ignition absence. By drawing absence samples near the location and time of historical ignitions, we better captured the complex environmental and anthropogenic conditions associated with fire occurrence or lack thereof. Models trained without stratified sampling produced ignition probability maps that consistently overestimated fire risk during high fire danger periods, whereas models incorporating stratified fire absence samples more accurately captured the spatial and temporal variability of fire potential and achieved predictive accuracies exceeding 95%. In addition to operational utility for fire prevention and resource allocation, our approach offers insights into the drivers of wildfire ignitions and highlights the value of incorporating spatial and temporal structure in absence sampling for wildfire modeling.

Short-duration precipitation extremes can threaten public safety and infrastructure by generating flash flooding and geophysical mass wasting events including mudslides and debris flows. Using two surface gauge-based precipitation datasets (1980-2024), we characterize the climatology of annual maximum subdaily precipitation and quantify trends across the western United States (WUS)-a topographically complex region with widely varying precipitation regimes prone to flash flooding. We find that 60.7% of WUS stations, including the vast majority of those located in the continental interior, typically experience 1-hr annual maximum precipitation (AMP) during summer and during the afternoon and evening hours (12:00-23:00 local time). Although most stations do not show statistically significant trends in 1-hr AMP intensity over the full period of record (1980-2024), a significant 10.3% domain-median increase in 1-hr AMP intensity was observed during 2000-2024. These changes largely result from seasonal maximum precipitation (SMP) increases during summer over the continental interior coinciding with a trend toward more favorable summer thermodynamic environments for short-duration precipitation extremes. We also report widespread though statistically insignificant increases in SMP intensity during winter and spring in California since 2000 coinciding with increased column water vapor. These results are suggestive of potential recent intensification of subdaily precipitation extremes in a warming climate in the WUS despite a backdrop of considerable internal variability.

Abstract Wildfire impacts on United States (US) communities have escalated in recent decades, highlighting the need to better understand factors that influence wildfire outcomes. We find that 462,069 homes were exposed to wildfires across the contiguous US during 2001–2020, two‐thirds of which occurred in the western US. Residential structure survivability—the percent of structures within a wildfire perimeter that were not destroyed by the fire—remained stable in the eastern US in the past two decades, but declined by 12% in the West coincident with a sixfold increase in the number of homes exposed to fire. Survivability was explained by structural age, fuels, and fire weather. Survivability to large fires in the West was lower for homes built pre‐1990 (84%) compared to post‐1990 homes (90%). Survivability was lower in forests compared to grasslands and shrublands. Finally, survivability was markedly lower for fires coincident with extreme fire weather. Our results suggest that modern building codes, fuel management, and proactive planning are pathways for strengthening wildfire resilience of the built environment.

We present an integrated drought impact assessment framework to capture the cascading effects of drought on water supply, agriculture, and the broader economy, using California’s 2020–2022 drought as a case study. The assessment was conducted as the drought unfolded, applying a top-down methodology to estimate changes in agricultural water supply, cropland adaptation responses, and spillover effects across downstream sectors. Despite data limitations, we demonstrate a replicable framework for predicting land fallowing and estimating economic impacts, relevant for timely drought assessments in California and other semi-arid irrigated regions worldwide. The framework leverages diverse, readily available datasets, including remote sensing based evapotranspiration estimates, records of reservoir storage and allocations, crop insurance claims, and regional economic statistics, combined with economic modeling tools. Our results indicate that in both 2021 and 2022, surface water deliveries in the Central Valley declined by about 43%, with varying spatial footprints. To partially offset these reductions, groundwater pumping rose by 51% in 2021 and 41% in 2022, with the largest increases occurring in the Tulare Lake region. These shifts resulted in an estimated 212 thousand hectares (8.2% reduction) of fallowed land in 2021 and 282 thousand hectares (10.9% reduction) in 2022, with direct crop revenue losses of $1.2 and $1.5 billion dollars, and 8.9 and 10.2 thousand jobs lost. Regional value added declined by $1.3 billion in 2021 and $1.9 billion in 2022 from both crop production and downstream food processing industries. Ex-post validation using newly released state water balance and crop mapping datasets shows that aggregate fallowing and water supply impacts were captured within −13% to +2% at the Central Valley scale, with more variable performance across individual crops and hydrologic regions. We highlight both the strengths and limitations of the framework while demonstrating its usefulness for timely drought impact assessment and mitigation planning.

California’s San Joaquin Valley (SJV) faces persistent water scarcity, climate stress, and environmental degradation. Land fallowing, or idling cropland, is one management response used to reduce irrigation demand during dry or restricted-water years. Because fallowing changes unevenly across space and time, it complicates water management, strategic land-use planning, and multi-benefit land repurposing. This study forecasts the spatial and temporal distribution of fallowed lands in the SJV using a hybrid machine-learning and deep-learning framework coupled with a cellular automata. Using observed LandIQ-based cropland/fallow maps (training period (2014–2020) for the 2020–2022 validation period, the deep learning based Fallow Prediction Framework (DL-FPF; F 1 ≈ 0.81; accuracy ≈ 0.94) outperformed classical machine-learning models ( F 1 ≈ 0.65–0.67; accuracy ≈ 0.88–0.89). Across the full SJV, DL-FPF identified 70 586.89 ha of gross crop-to-fallow transition from WY2022 to WY2024, equivalent to ≈25% of the WY2022 mapped fallow baseline. The final WY2024 map predicted 265 657.60 ha of Idle/Fallow land, 20 343.04 ha lower than WY2022 (−7.11%). An independent WY2024 LandIQ overlay showed moderate agreement with the DL-FPF prediction, with 10-class accuracy of 0.73, Cohen’s Kappa of 0.66, binary fallow/non-fallow F1 of 0.56, and binary IoU of 0.39; DL-FPF overestimated WY2024 fallow area by 13.34% relative to LandIQ. Gross transition pressure was highest in GW-only GSAs (31.67%), followed by SW-only GSAs (28.87%) and SW + GW GSAs (23.77%), while SW + GW GSAs had the largest absolute gross transition area. By separating transition magnitude, mapped WY2024 fallow area, and reference-map agreement, DL-FPF outputs can better support water allocation, cropping decisions, and multi-benefit land repurposing under the sustainable groundwater management act.

Abstract Rainfall and vapor pressure deficit (VPD) are well-studied hydrological variables that largely determine aboveground net primary production (ANPP) in most ecosystems. Meanwhile, the impacts of another important part of the hydrologic cycle, non-rainfall water from fog and dew, remain poorly understood at the ecosystem level. To fill this gap, we used meteorological variables measured at weather stations along with satellite-derived vegetation greenness data from surrounding areas to examine how fog and dew frequency affect summer plant growth across the contiguous United States. Our analysis shows that, even after accounting for precipitation, VPD, and land-cover type, fog and, more so, dew enhanced vegetation productivity in water-limited regions. In contrast, non-rainfall water had a neutral or negative impact on plant growth in humid regions, with fog showing the strongest and most widespread negative effects. Taken together, our findings reveal that summertime non-rainfall water has differential effects on vegetation that are largely determined by ecosystem-level water availability. These aridity-dependent effects of fog and dew should be considered in future ecological and agricultural studies and in assessments of projected climate impacts on vegetation.

Climatological studies of downslope windstorms (DWs) in the Scandinavian mountains (Scandes) are rare. Here we use 20-year long simulations with the kilometre-scale regional climate model HCLIM38-AROME to study DWs in the Scandes in present and future climate, their connection to large-scale atmospheric circulation, and their cooling and warming effect. A DW is identified in a model grid point using a two-step conceptual model based on terrain features, and dynamic and thermodynamic variables. We find that DWs occur most frequently in winter, which is therefore the focal season for the analyses. Normally, DWs are predominantly either warming (foehn type) or cooling (bora type), but our results indicate that this distinction does not hold for the DWs in the Scandes. Even though there is a slight overall tendency towards warming, almost all locations with DWs can experience both cooling and warming. The future simulations using two parent global climate models (GCMs) show an overall decrease in the total number of DW occurrences between 7% and 17% toward the end of the century, with a decrease in the northern and central parts of the Scandes and an increase in the southeastern part. These changes can be attributed to two factors: the frequency change of certain circulation types and internal changes within the circulation types. Despite the agreement in the sign of future changes for both GCM forcings, the difference in the contribution of the two factors indicates that different mechanisms are responsible for the total future change in the DW occurrence. The complexity of DWs in the Scandes and their future change indicates that kilometre-scale or finer climate models are required for a proper depiction of DWs and that a larger ensemble of simulations with different GCM forcing is required for evaluation of different mechanisms of the future change.