A US-France research team published in Physics World an AI-augmented rare-event sampling (RES) climate simulation framework that uses AI to pre-screen meteorological fields likely to spawn heatwaves or extreme cyclones and runs full physical simulations only for those high-risk windows, cutting compute to about 1/1,000 of full-domain simulation. Benchmarked on the PlaSim simplified climate model, the method produced mid-latitude heatwave frequency estimates closely matching full simulations and

A US-France research team published in Physics World an AI-augmented rare-event sampling (RES) climate simulation framework that uses AI to pre-screen meteorological fields likely to spawn heatwaves or extreme cyclones and runs full physical simulations only for those high-risk windows, cutting compute to about 1/1,000 of full-domain simulation. Benchmarked on the PlaSim simplified climate model, the method produced mid-latitude heatwave frequency estimates closely matching full simulations and is applicable to analysis of current European heatwaves. ETH Zurich researchers say the approach remedies narrow screening in traditional RES, can be expanded to heavy rainfall and typhoon prediction, and is adaptable to higher-resolution climate models.