Early warning of complex climate risk with integrated artificial intelligence

Reichstein, Markus; Benson, Vitus; Blunk, Jan; Camps-Valls, Gustau; Creutzig, Felix; Fearnley, Carina J.; Han, Boran; Kornhuber, Kai; Rahaman, Nasim; Schölkopf, Bernhard; Tárraga, José María; Vinuesa, Ricardo; Dall, Karen; Denzler, Joachim; Frank, Dorothea; Martini, Giulia; Nganga, Naomi; Maddix, Danielle C.; Weldemariam, Kommy

doi:10.1038/s41467-025-57640-w

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Early warning of complex climate risk with integrated artificial intelligence

Authors

Reichstein, Markus
External Organizations;

Benson, Vitus
External Organizations;

Blunk, Jan
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Camps-Valls, Gustau
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/persons/resource/Felix.Creutzig

Creutzig, Felix
Potsdam Institute for Climate Impact Research;

Fearnley, Carina J.
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Han, Boran
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Kornhuber, Kai
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Rahaman, Nasim
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Schölkopf, Bernhard
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Tárraga, José María
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Vinuesa, Ricardo
External Organizations;

Dall, Karen
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Denzler, Joachim
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Frank, Dorothea
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Martini, Giulia
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Nganga, Naomi
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Maddix, Danielle C.
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Weldemariam, Kommy
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Citation

Reichstein, M., Benson, V., Blunk, J., Camps-Valls, G., Creutzig, F., Fearnley, C. J., Han, B., Kornhuber, K., Rahaman, N., Schölkopf, B., Tárraga, J. M., Vinuesa, R., Dall, K., Denzler, J., Frank, D., Martini, G., Nganga, N., Maddix, D. C., Weldemariam, K. (2025): Early warning of complex climate risk with integrated artificial intelligence. - Nature Communications, 16, 2564.
https://doi.org/10.1038/s41467-025-57640-w

Cite as: https://publications.pik-potsdam.de/pubman/item/item_32995

Abstract

As climate change accelerates, human societies face growing exposure to disasters and stress, highlighting the urgent need for effective early warning systems (EWS). These systems monitor, assess, and communicate risks to support resilience and sustainable development, but challenges remain in hazard forecasting, risk communication, and decision-making. This perspective explores the transformative potential of integrated Artificial Intelligence (AI) modeling. We highlight the role of AI in developing multi-hazard EWSs that integrate Meteorological and Geospatial foundation models (FMs) for impact prediction. A user-centric approach with intuitive interfaces and community feedback is emphasized to improve crisis management. To address climate risk complexity, we advocate for causal AI models to avoid spurious predictions and stress the need for responsible AI practices. We highlight the FATES (Fairness, Accountability, Transparency, Ethics, and Sustainability) principles as essential for equitable and trustworthy AI-based Early Warning Systems for all. We further advocate for decadal EWSs, leveraging climate ensembles and generative methods to enable long-term, spatially resolved forecasts for proactive climate adaptation.