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arXiv · 2412.08079

Regional climate risk assessment from climate models using probabilistic machine learning

Zhong Yi Wan·Ignacio Lopez-Gomez·Robert Carver·Tapio Schneider·John Anderson·Fei Sha·Leonardo Zepeda-Núñez
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Abstract

Effective climate risk assessment is hindered by the resolution gap between coarse global climate models and the fine-scale information needed for regional decisions. We introduce GenFocal, an AI framework that generates statistically accurate, fine-scale weather from coarse climate projections, without requiring paired simulated and observed events during training. GenFocal synthesizes complex and long-lived hazards, such as heat waves and tropical cyclones, even when they are not well represented in the coarse climate projections. It also samples high-impact, rare events more accurately than leading methods. By translating large-scale climate projections into actionable, localized information, GenFocal provides a powerful new paradigm to improve climate adaptation and resilience strategies.

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Zhong Yi Wan, Ignacio Lopez-Gomez, Robert Carver, Tapio Schneider, John Anderson, Fei Sha, Leonardo Zepeda-Núñez. 2024-12-11. Regional climate risk assessment from climate models using probabilistic machine learning. https://arxiv.org/abs/2412.08079

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