Different drivers have been shown to play a central role in modulating the occurrence and intensity of summer temperature extremes, yet their individual contributions remain difficult to quantify. In this study, we develop an explainable machine‐learning framework to disentangle the respective influences of large‐scale atmospheric circulation, soil‐moisture anomalies, and rising CO₂ concentrations on boreal‐summer temperature extremes at six locations across Europe and North Africa with different characteristics of land–atmosphere coupling (Córdoba, Lyon, Hannover, Stockholm, Belgrade, and Marrakech). Using SHapley Additive exPlanation (SHAP) values, we find that the atmospheric circulation consistently dominates model explainability across all locations, contributing to 67–90 % of the total mean SHAP, with the geopotential at 500 hPa field contributing the most. Soil‐moisture influence exhibits a northward gradient: negligible at Marrakech (0.5 %), moderate at Córdoba (7.7 %), and substantial at Lyon (15 %). Additionally, negative correlations between soil‐moisture standardized anomalies and SHAP values across three depth levels corroborate the amplifying effect of land drying on heat extremes. We demonstrate the robustness of these findings to a less stringent (80th percentile) extreme definition. Furthermore, the identified driver contributions are consistent when using alternative observational data for temperature extreme definition and for computing SPI/SPEI drought indices as proxies for soil moisture, with SPEI showing a closer alignment to the original ERA5-Land results. We also illustrate the methodology for case studies of two individual events, heatwaves occurring in Córdoba (Spain) 2021 and Hannover (Germany) 2018, which reveal a pronounced spatial pattern in the distribution of SHAP values for the circulation predictors. They also confirm the enhanced role of the land component in regions of Northern Europe and reveal a contribution of the anthropogenic factor through CO₂ concentrations, even for specific events. These insights enhance our understanding of the physical mechanisms behind temperature extremes and demonstrate the potential of explainable artificial intelligence methods to quantify the contributions from different drivers of hot temperature extremes.