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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 During the last week of June 2021, the Pacific Northwest region of North America experienced a record-breaking heatwave of historic proportions. All-time high temperature records were shattered, often by several degrees, across many locations, with Canada setting a new national record, the state of Washington setting a new record, and the state of Oregon tying its previous record. Here we diagnose key meteorology that contributed to this heatwave. The event was associated with a highly anomalous midtropospheric ridge, with peak 500-hPa geopotential height anomalies centered over central British Columbia. This ridge developed over several days as part of a large-scale wave train. Back trajectory analysis indicates that synoptic-scale subsidence and associated adiabatic warming played a key role in enhancing the magnitude of the heat to the south of the ridge peak, while diabatic heating was dominant closer to the ridge center. Easterly/offshore flow inhibited marine cooling and contributed additional downslope warming along the western portions of the region. A notable surface thermally induced trough was evident throughout the event over western Oregon and Washington. An eastward shift of the thermal trough, following the eastward migration of the 500-hPa ridge, allowed an inland surge of cooler marine air and dramatic 24-h cooling, especially along the western periphery of the region. Large-scale horizontal warm-air advection played a minimal role. When compared with past highly amplified ridges over the region, this event was characterized by much higher 500-hPa geopotential heights, a stronger thermal trough, and stronger offshore flow. 

Abstract Simultaneous heatwaves affecting multiple regions (referred to as concurrent heatwaves) pose compounding threats to various natural and societal systems, including global food chains, emergency response systems, and reinsurance industries. While anthropogenic climate change is increasing heatwave risks across most regions, the interactions between warming and circulation changes that yield concurrent heatwaves remain understudied. Here, we quantify historical (1979–2019) trends in concurrent heatwaves during the warm season [May–September (MJJAS)] across the Northern Hemisphere mid- to high latitudes. We find a significant increase of ∼46% in the mean spatial extent of concurrent heatwaves and ∼17% increase in their maximum intensity, and an approximately sixfold increase in their frequency. Using self-organizing maps, we identify large-scale circulation patterns (300 hPa) associated with specific concurrent heatwave configurations across Northern Hemisphere regions. We show that observed changes in the frequency of specific circulation patterns preferentially increase the risk of concurrent heatwaves across particular regions. Patterns linking concurrent heatwaves across eastern North America, eastern and northern Europe, parts of Asia, and the Barents and Kara Seas show the largest increases in frequency (∼5.9 additional days per decade). We also quantify the relative contributions of circulation pattern changes and warming to overall observed concurrent heatwave day frequency trends. While warming has a predominant and positive influence on increasing concurrent heatwave frequency, circulation pattern changes have a varying influence and account for up to 0.8 additional concurrent heatwave days per decade. Identifying regions with an elevated risk of concurrent heatwaves and understanding their drivers is indispensable for evaluating projected climate risks on interconnected societal systems and fostering regional preparedness in a changing climate. Significance StatementHeatwaves pose a major threat to human health, ecosystems, and human systems. Simultaneous heatwaves affecting multiple regions can exacerbate such threats. For example, multiple food-producing regions simultaneously undergoing heat-related crop damage could drive global food shortages. We assess recent changes in the occurrence of simultaneous large heatwaves. Such simultaneous heatwaves are 7 times more likely now than 40 years ago. They are also hotter and affect a larger area. Their increasing occurrence is mainly driven by warming baseline temperatures due to global heating, but changes in weather patterns contribute to disproportionate increases over parts of Europe, the eastern United States, and Asia. Better understanding the drivers of weather pattern changes is therefore important for understanding future concurrent heatwave characteristics and their impacts.