# Regional climate risk assessment from climate models using probabilistic machine learning

arXiv: https://arxiv.org/abs/2412.08079 · HTML: https://mlsanity.com/paper/2412.08079/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2024.

## Built on by (most cited first, 7 of 7)

- [FLEX: A Backbone for Diffusion-Based Modeling of Spatio-temporal Physical Systems](https://mlsanity.com/paper/2505.17351/) (2025)
- [Universal Diffusion-Based Probabilistic Downscaling](https://mlsanity.com/paper/2602.11893/) (2026)
- [Downscaling weather forecasts from Low- to High-Resolution with Diffusion Models](https://mlsanity.com/paper/2604.03303/) (2026)
- [STLDM: Spatio-Temporal Latent Diffusion Model for Precipitation Nowcasting](https://mlsanity.com/paper/2512.21118/) (2025)
- [How Can Machine Learning Emulators Best Support Climate Science?](https://mlsanity.com/paper/2603.22320/) (2026)
- [IPSL-AID: Generative Diffusion Models for Climate Downscaling from Global to Regional Scales](https://mlsanity.com/paper/2604.03275/) (2026)
- [DySCo: Dynamically consistent data-driven downscaling of extremes in climate projections](https://mlsanity.com/paper/2608.21998/) (2026)

## Abstract (arXiv)

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.