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

Viewed

Total article views: 3,370 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,283 890 197 3,370 171 155
  • HTML: 2,283
  • PDF: 890
  • XML: 197
  • Total: 3,370
  • BibTeX: 171
  • EndNote: 155
Views and downloads (calculated since 19 Nov 2025)
Cumulative views and downloads (calculated since 19 Nov 2025)

Viewed (geographical distribution)

Total article views: 3,370 (including HTML, PDF, and XML) Thereof 3,318 with geography defined and 52 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 07 Sep 2026
Download
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.
Share