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arXiv · 2510.27066

AI-boosted rare event sampling to characterize extreme weather

Amaury Lancelin·Alex Wikner·Laurent Dubus·Clément Le Priol·Dorian S. Abbot·Freddy Bouchet·Pedram Hassanzadeh·Jonathan Weare
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Abstract

Weather extremes pose major societal risks, especially in a changing climate, but due to their rarity, they are difficult to study using limited observations or complex climate models. We introduce AI+RES, a framework coupling fast AI weather forecasts with a high-fidelity physics model using a rare-event algorithm to efficiently characterize extremes. This approach enables the study of the statistics and physics of very rare events, such as once per millennium heatwaves at two orders-of-magnitude lower computational cost. AI+RES can be applied broadly across climate science and other fields concerned with rare events.

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BibTeXRIS

Amaury Lancelin, Alex Wikner, Laurent Dubus, Clément Le Priol, Dorian S. Abbot, Freddy Bouchet, Pedram Hassanzadeh, Jonathan Weare. 2025-10-31. AI-boosted rare event sampling to characterize extreme weather. https://doi.org/10.1103/b1gc-9c2q

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