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  Bridging the weather and climate divide with artificial intelligence

Camps-Valls, G., Carrassi, A., de Melo Viríssimo, F., Debeire, K., Fernández-Torres, M.-Á., Hess, P., Heuer, H., Mankovich, N., Montero, D., Rodrigo-Bonet, E., Serva, F., Sitokonstantinou, V., White, A., Cohrs, K.-H., Wider, J., Reimers, C., Bountos, N. I., Papoutsis, I., Gentine, P., Eyring, V. (2026): Bridging the weather and climate divide with artificial intelligence. - Nature Communications, 17, 8578.
https://doi.org/10.1038/s41467-026-75787-y

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Camps-Valls, Gustau1, Author
Carrassi, Alberto1, Author
de Melo Viríssimo, Francisco1, Author
Debeire, Kevin1, Author
Fernández-Torres, Miguel-Ángel1, Author
Hess, Philipp2, Author                 
Heuer, Helge1, Author
Mankovich, Nathan1, Author
Montero, David1, Author
Rodrigo-Bonet, Esther1, Author
Serva, Federico1, Author
Sitokonstantinou, Vasileios1, Author
White, Alistair2, Author                 
Cohrs, Kai-Hendrik1, Author
Wider, Jonathan1, Author
Reimers, Christian1, Author
Bountos, Nikolaos Ioannis1, Author
Papoutsis, Ioannis1, Author
Gentine, Pierre1, Author
Eyring, Veronika1, Author
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: Weather and climate shape every aspect of our lives, yet for decades they have been studied as separate worlds—one concerned with what happens tomorrow, the other with what awaits us over the coming decades. This long-standing divide has slowed progress in both fields. Here, we argue that artificial intelligence (AI) is helping to dissolve the boundary between them. By learning directly from vast streams of Earth observations, AI is starting to predict both tomorrow’s storm and next century’s climate within a single, unified framework. We show how this convergence is already underway, why weather and climate science have much to gain from each other, and what still stands in the way—from trust, transparency, and fairness to the energy cost of computation and the risk of widening global inequalities. Realizing this vision will demand not only better algorithms but a new culture of collaboration across scientific communities, operational centers, fields and disciplines. Done well, a unified, AI-driven Earth science could deliver faster, more reliable, and more actionable predictions—helping societies anticipate and adapt to a rapidly warming planet.

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Language(s): eng - English
 Dates: 2026-08-182026-08-18
 Publication Status: Finally published
 Pages: 16
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1038/s41467-026-75787-y
MDB-ID: No data to archive
OATYPE: Gold Open Access
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Working Group: Artificial Intelligence
 Degree: -

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Title: Nature Communications
Source Genre: Journal, SCI, Scopus, oa
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Pages: - Volume / Issue: 17 Sequence Number: 8578 Start / End Page: - Identifier: Publisher: Nature
CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journals354