Artificial intelligence for modeling and understanding extreme weather and climate events

Gustau Camps-Valls (), Miguel-Ángel Fernández-Torres, Kai-Hendrik Cohrs, Adrian Höhl, Andrea Castelletti, Aytac Pacal, Claire Robin, Francesco Martinuzzi, Ioannis Papoutsis, Ioannis Prapas, Jorge Pérez-Aracil, Katja Weigel, Maria Gonzalez-Calabuig, Markus Reichstein, Martin Rabel, Matteo Giuliani, Miguel D. Mahecha, Oana-Iuliana Popescu, Oscar J. Pellicer-Valero, Said Ouala, Sancho Salcedo-Sanz, Sebastian Sippel, Spyros Kondylatos, Tamara Happé and Tristan Williams
Additional contact information
Gustau Camps-Valls: Universitat de València
Miguel-Ángel Fernández-Torres: Universitat de València
Kai-Hendrik Cohrs: Universitat de València
Adrian Höhl: Technical University of Munich
Andrea Castelletti: Politecnico di Milano
Aytac Pacal: Institut für Physik der Atmosphäre
Claire Robin: Max Planck Institute of Biogeochemistry
Francesco Martinuzzi: Leipzig University
Ioannis Papoutsis: National Technical University of Athens
Ioannis Prapas: Universitat de València
Jorge Pérez-Aracil: Universidad de Alcalá
Katja Weigel: Institut für Physik der Atmosphäre
Maria Gonzalez-Calabuig: Universitat de València
Markus Reichstein: Max Planck Institute of Biogeochemistry
Martin Rabel: Institute for Data Science
Matteo Giuliani: Politecnico di Milano
Miguel D. Mahecha: Leipzig University
Oana-Iuliana Popescu: Institute for Data Science
Oscar J. Pellicer-Valero: Universitat de València
Said Ouala: UMR CNRS 6285 & INRIA team odyssey
Sancho Salcedo-Sanz: Universidad de Alcalá
Sebastian Sippel: Leipzig University
Spyros Kondylatos: Universitat de València
Tamara Happé: VU Amsterdam
Tristan Williams: Universitat de València

Nature Communications, 2025, vol. 16, issue 1, 1-14

Abstract: Abstract In recent years, artificial intelligence (AI) has deeply impacted various fields, including Earth system sciences, by improving weather forecasting, model emulation, parameter estimation, and the prediction of extreme events. The latter comes with specific challenges, such as developing accurate predictors from noisy, heterogeneous, small sample sizes and data with limited annotations. This paper reviews how AI is being used to analyze extreme climate events (like floods, droughts, wildfires, and heatwaves), highlighting the importance of creating accurate, transparent, and reliable AI models. We discuss the hurdles of dealing with limited data, integrating real-time information, and deploying understandable models, all crucial steps for gaining stakeholder trust and meeting regulatory needs. We provide an overview of how AI can help identify and explain extreme events more effectively, improving disaster response and communication. We emphasize the need for collaboration across different fields to create AI solutions that are practical, understandable, and trustworthy to enhance disaster readiness and risk reduction.

Date: 2025
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DOI: 10.1038/s41467-025-56573-8

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