Articles | Volume 7, issue 3
https://doi.org/10.5194/wcd-7-1709-2026
https://doi.org/10.5194/wcd-7-1709-2026
Research article
 | 
07 Sep 2026
Research article |  | 07 Sep 2026

Quantifying atmospheric and land drivers of hot temperature extremes through explainable Artificial Intelligence

Arnau Garcia Mesa, Lluís Palma, Markus Donat, Stefano Materia, Bruna Gràvalos Talló, and Raül Marcos Matamoros

Data sets

Dataset for Quantifying Atmospheric and Land Drivers of hot tempERAture extremes through explainable Artificial Intelligence Arnau Garcia Mesa https://doi.org/10.5281/zenodo.21337082

Model code and software

agarcimes8/QuantifyDriversHW: v1.0 (Version v1.0) Bruna Gràvalos and Arnau Garcia Mesa https://doi.org/10.5281/zenodo.22143255

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Short summary
Explainable Artificial Intelligence was used to quantify and disentangle the role of the drivers of summer heat extremes at six sites in Europe and North Africa. Large-scale atmospheric circulation, especially mid-tropospheric geopotential, dominates, while soil drying amplifies heat in temperate and northern regions, and rising carbon dioxide contributes to long-term trends. Results were robust across data sources and help target climate risk and model improvements.
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