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.
@article{arxiv.2412.08079,
title = {Regional climate risk assessment from climate models using probabilistic machine learning},
author = {Zhong Yi Wan and Ignacio Lopez-Gomez and Robert Carver and Tapio Schneider and John Anderson and Fei Sha and Leonardo Zepeda-Núñez},
journal= {arXiv preprint arXiv:2412.08079},
year = {2026}
}