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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 Cloud‐to‐ground lightning with minimal rainfall (“dry” lightning) is a major wildfire ignition source in the western United States (WUS). Although dry lightning is commonly defined as occurring with <2.5 mm of daily‐accumulated precipitation, a rigorous quantification of precipitation amounts concurrent with lightning‐ignited wildfires (LIWs) is lacking. We combine wildfire, lightning and precipitation data sets to quantify these ignition precipitation amounts across ecoprovinces of the WUS. The median precipitation for all LIWs is 2.8 mm but varies with vegetation and fire characteristics. “Holdover” fires not detected until 2–5 days following ignition occur with significantly higher precipitation (5.1 mm) compared to fires detected promptly after ignition (2.5 mm), and with cooler and wetter environmental conditions. Further, there is substantial variation in precipitation associated with promptly‐detected (1.7–4.6 mm) and holdover (3.0–7.7 mm) fires across ecoprovinces. Consequently, the widely‐used 2.5 mm threshold does not fully capture lightning ignition risk and incorporating ecoprovince‐specific precipitation amounts would better inform WUS wildfire prediction and management.