GenCast: diffusion-based ensemble forecast beats ECMWF's ENS on 97% of targets
GenCast (Nature, Dec 2024) is a diffusion model producing probabilistic 15-day ensemble forecasts. It beat ECMWF's ENS, the leading operational ensemble, on 97.2% of 1,320 targets and on 99.8% at lead times beyond 36 hours, generating a 15-day ensemble member in about 8 minutes on one TPU.
Key facts
- 97.2% of 1,320 targets better than ENS; 99.8% beyond 36 h
- Better prediction of extreme weather, tropical-cyclone tracks and wind-power output
- Code and weights released for research
Science result
- Field
- climate-weather / probabilistic forecasting
- Problem
- Ensemble (probabilistic) medium-range weather forecasting
- Result
- First ML ensemble system to outperform the top operational ensemble on the vast majority of targets.
- AI system
- GenCast
- Human role
- Human-designed model
- Verification
- Peer-reviewed in Nature
- Status
- confirmed
What happened
DeepMind applied image-style diffusion to the atmosphere, sampling many plausible futures rather than one.
Why it matters
Ensembles drive decisions about extreme-weather risk. AI now leads here too, feeding into the WeatherNext models used by forecasters.
Changelog
- 2026-09-29: created
Related events
- GraphCast: ML weather model beats the world's best physics-based 10-day forecast on 90% of targets ★★★★
- DeepMind open-sources WeatherNext 2 and WeatherNext Cyclones with a Nature paper showing ~1 extra day of hurricane warning ★★★
- NeuralGCM: Google's hybrid physics-ML atmosphere model matches top weather forecasts and runs decades-long climate simulations ★★★
Sources (2)
id: 2024-12-04-gencast-ensemble-weather · updated 2026-09-29 · open in the interactive timeline