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Abstract Lightning is a major source of wildfire ignition in the western United States (WUS). We build and train convolutional neural networks (CNNs) to predict the occurrence of cloud‐to‐ground (CG) lightning across the WUS during June–September from the spatial patterns of seven large‐scale meteorological variables from reanalysis (1995–2022). Individually trained CNN models at each 1° × 1° grid cell (n = 285 CNNs) show high skill at predicting CG lightning days across the WUS (median AUC = 0.8) and perform best in parts of the interior Southwest where summertime CG lightning is most common. Further, interannual correlation between observed and predicted CG lightning days is high (medianr = 0.87), demonstrating that locally trained CNNs realistically capture year‐to‐year variation in CG lightning activity across the WUS. We then use layer‐wise relevance propagation (LRP) to investigate the relevance of predictor variables to successful CG lightning prediction in each grid cell. Using maximum LRP values, our results show that two thermodynamic variables—ratio of surface moist static energy to free‐tropospheric saturation moist static energy, and the 700–500 hPa lapse rate—are the most relevant CG lightning predictors for 93%–96% of CNNs depending on the LRP variant used. As lightning is not directly simulated by global climate models, these CNNs could be used to parameterize CG lightning in climate models to assess changes in future CG lightning occurrence with projected climate change. Understanding changes in CG lightning risk and consequently lightning‐caused wildfire risk across the WUS could inform fire management, planning, and disaster preparedness. 

Abstract Climate change poses growing risks to global agriculture including perennial tree fruit such as apples that hold important nutritional, cultural, and economic value. This study quantifies historical trends in climate metrics affecting apple growth, production, and quality, which remain understudied. Utilizing the high-resolution gridMET dataset, we analyzed trends (1979–2022) in several key metrics across the U.S.—cold degree days, chill portions, last day of spring frost, growing degree days (GDD), extreme heat days (daily maximum temperature >34 °C), and warm nights (daily minimum temperatures >15 °C). We found significant trends across large parts of the U.S. in all metrics, with the spatial patterns consistent with pronounced warming across the western states in summer and winter. Yakima County, WA, Kent County, MI, Wayne County, NY—leading apple-producers—showed significant decreasing trends in cold degree days and increasing trends in GDD and warm fall nights. Yakima county, with over 48 870 acres of apple orchards, showed significant changes in five of the six metrics—earlier last day of spring frost, fewer cold degree days, increasing GDD over the overall growth period, and more extreme heat days and warm nights. These trends could negatively affect apple production by reducing the dormancy period, altering bloom timing, increasing sunburn risk, and diminishing apple appearance and quality. Large parts of the U.S. experience detrimental trends in multiple metrics simultaneously that indicate the potential for compounding negative impacts on the production and quality of apples and other tree fruit, emphasizing the need for developing and adopting adaptation strategies. 

Abstract Natural climate phenomena like El Niño Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) influence the Indian monsoon and thereby the region’s agricultural systems. Understanding their influence can provide seasonal predictability of agricultural production metrics to inform decision-making and mitigate potential food security challenges. Here, we analyze the effects of ENSO and IOD on four agricultural production metrics (production, harvested area, irrigated area, and yields) for rice, maize, sorghum, pearl millet, and finger millet across India from 1968 to 2015. El Niños and positive-IODs are associated with simultaneous reductions in the production and yields of multiple crops. Impacts vary considerably by crop and geography. Maize and pearl millet experience large declines in both production and yields when compared to other grains in districts located in the northwest and southern peninsular regions. Associated with warmer and drier conditions during El Niño, >70% of all crop districts experience lower production and yields. Impacts of positive-IODs exhibit relatively more spatial variability. La Niña and negative-IODs are associated with simultaneous increases in all production metrics across the crops, particularly benefiting traditional grains. Variations in impacts of ENSO and IOD on different cereals depend on where they are grown and differences in their sensitivity to climate conditions. We compare production metrics for each crop relative to rice in overlapping rainfed districts to isolate the influence of climate conditions. Maize production and yields experience larger reductions relative to rice, while pearl millet production and yields also experience reductions relative to rice during El Niños and positive-IODs. However, sorghum experiences enhanced production and harvested areas, and finger millet experiences enhanced production and yields. These findings suggest that transitioning from maize and rice to these traditional cereals could lower interannual production variability associated with natural climate variations. 

During summer 2010, exceptional heat and drought in western Russia (WRU) occurred simultaneously with heavy rainfall and flooding in northern Pakistan (NPK). Here, we use the Great Eurasian Drought Atlas (GEDA), a new 1,021 year tree-ring reconstruction of summer soil moisture, to investigate the variability and dynamics of this exceptional spatially concurrent climate extreme over the last millennium. Summer 2010 in the GEDA was the second driest year over WRU and the largest wet–dry contrast between NPK and WRU; it was also the second warmest year over WRU in an independent 1,015 year temperature reconstruction. Soil moisture variability is only weakly correlated between the two regions and 2010 event analogues are rare, occurring in 31 (3.0%) or 52 (5.1%) years in the GEDA, depending on the definition used. Post-1900 is significantly drier in WRU and wetter in NPK compared to previous centuries, increasing the likelihood of concurrent wet NPK–dry WRU extremes, with over 20% of the events in the record occurring in this interval. The dynamics of wet NPK–dry WRU events like 2010 are well captured by two principal components in the GEDA, modes correlated with ridging over northern Europe and western Russia and a pan-hemispheric extratropical wave train pattern similar to that observed in 2010. Our results highlight how high resolution paleoclimate reconstructions can be used to capture some of the most extreme events in the climate system, investigate their physical drivers, and allow us to assess their behavior across longer timescales than available from shorter instrumental records. 

Abstract Several recent widespread temperature extremes across the United States (U.S.) have been associated with power outages, disrupting access to electricity at times that are critical for the health and well-being of communities. Building resilience to such extremes in our energy infrastructure needs a comprehensive understanding of their spatial and temporal characteristics. In this study, we systematically quantify the frequency, extent, duration, and intensity of widespread temperature extremes and their associated energy demand in the six North American Electric Reliability Corporation regions using ERA5 reanalysis data. We show that every region has experienced hot or cold extremes that affected nearly their entire extent and such events were associated with substantially higher energy demand, resulting in simultaneous stress across the entire electric gird. The western U.S. experienced significant increases in the frequency (123%), extent (32%), duration (55%) and intensity (29%) of hot extremes and Texas experienced significant increases in the frequency (132%) of hot extremes. The frequency of cold extremes has decreased across most regions without substantial changes in other characteristics. Using power outage data, we show that recent widespread extremes in nearly every region have coincided with power outages, and such outages account for between 12%–52% of all weather-related outages in the past decade depending on the region. Importantly, we find that solar potential is significantly higher during widespread hot extremes in all six regions and during widespread cold extremes in five of the six regions. Further, wind potential is significantly higher during widespread hot or cold extremes in at least three regions. Our findings indicate that increased solar and wind capacity could be leveraged to meet the higher demand for energy during such widespread extremes, improving the resilience and reliability of our energy systems in addition to limiting carbon emissions. 

Escalating wildfire activity in the western United States has accelerated adverse societal impacts. Observed increases in wildfire severity and impacts to communities have diverse anthropogenic causes—including the legacy of fire suppression policies, increased development in high-risk zones, and aridification by a warming climate. However, the intentional use of fire as a vegetation management tool, known as “prescribed fire,” can reduce the risk of destructive fires and restore ecosystem resilience. Prescribed fire implementation is subject to multiple constraints, including the number of days characterized by weather and vegetation conditions conducive to achieving desired outcomes. Here, we quantify observed and projected trends in the frequency and seasonality of western United States prescribed fire days. We find that while ~2 C of global warming by 2060 will reduce such days overall (−17%), particularly during spring (−25%) and summer (−31%), winter (+4%) may increasingly emerge as a comparatively favorable window for prescribed fire especially in northern states. 

Abstract June 2023 witnessed the hottest, largest, and longest‐lasting heatwave across Mexico and Texas between 1940 and 2023. We apply constructed analogs with multiple linear regression models to quantify the contribution of different drivers to daily temperature anomalies during this heatwave. On the hottest day (20 June), circulation, soil moisture, and their interaction explained 3.82°C (90% CI: 2.72–4.91°C) of the 5.42°C observed anomaly with most of the residual attributed to the thermodynamic effects of long‐term warming. Using CESM2‐LENS2, we find that June 2023‐like patterns are not projected to increase in frequency but will become 1.9°C hotter by the mid‐21st century under SSP3‐7.0. The hottest simulated day with these patterns could produce temperatures >50°C (122°F) across south Texas, representing a low‐likelihood yet physically plausible worst‐case scenario that could inform disaster preparedness and adaptation planning. 

Abstract Increases in population exposure to humid heat extremes in agriculturally-dependent areas of the world highlights the importance of understanding how the location and timing of humid heat extremes intersects with labor-intensive agricultural activities. Agricultural workers are acutely vulnerable to heat-related health and productivity impacts as a result of the outdoor and physical nature of their work and by compounding socio-economic factors. Here, we identify the regions, crops, and seasons when agricultural workers experience the highest hazard from extreme humid heat. Using daily maximum wet-bulb temperature data, and region-specific agricultural calendars and cropland area for 12 crops, we quantify the number of extreme humid heat days during the planting and harvesting seasons for each crop between 1979–2019. We find that rice, an extremely labor-intensive crop, and maize croplands experienced the greatest exposure to dangerous humid heat (integrating cropland area exposed to >27 °C wet-bulb temperatures), with 2001–2019 mean rice and maize cropland exposure increasing 1.8 and 1.9 times the 1979–2000 mean exposure, respectively. Crops in socio-economically vulnerable regions, including Southeast Asia, equatorial South America, the Indo-Gangetic Basin, coastal Mexico, and the northern coast of the Gulf of Guinea, experience the most frequent exposure to these extremes, in certain areas exceeding 60 extreme humid heat days per year when crops are being cultivated. They also experience higher trends relative to other world regions, with certain areas exceeding a 15 day per decade increase in extreme humid heat days. Our crop and location-specific analysis of extreme humid heat hazards during labor-intensive agricultural seasons can inform the design of policies and efforts to reduce the adverse health and productivity impacts on this vulnerable population that is crucial to the global food system. 

Abstract The impact of extreme heat on crop yields is an increasingly pressing issue given anthropogenic climate warming. However, some of the physical mechanisms involved in these impacts remain unclear, impeding adaptation-relevant insight and reliable projections of future climate impacts on crops. Here, using a multiple regression model based on observational data, we show that while extreme dry heat steeply reduced U.S. corn and soy yields, humid heat extremes had insignificant impacts and even boosted yields in some areas, despite having comparably high dry-bulb temperatures as their dry heat counterparts. This result suggests that conflating dry and humid heat extremes may lead to underestimated crop yield sensitivities to extreme dry heat. Rainfall tends to precede humid but not dry heat extremes, suggesting that multivariate weather sequences play a role in these crop responses. Our results provide evidence that extreme heat in recent years primarily affected yields by inducing moisture stress, and that the conflation of humid and dry heat extremes may lead to inaccuracy in projecting crop yield responses to warming and changing humidity.