AI-Boosted Rare Event Sampling to Characterize Extreme Weather
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 heat waves at two orders-of-magnitude lower computational cost. AI+RES can be applied broadly across climate science and other fields concerned with rare events.
Fields of Study & Subjects
Probability and Risk Models
stochastic dynamics and bifurcation
Financial Risk and Volatility Modeling
Publication details
- Journal / source
- Physical Review Letters
- Publisher
- American Physical Society (APS)
- Volume
- 137
- Issue
- 6
- Pages
- 064201–064201
- Publication date
- 8 June 2026
- Language
- EN