GenCast: diffusion-based ensemble forecast beats ECMWF’s ENS on 97% of targets
Result confirmed
The takeaway
GenCast (Nature, Dec 2024) is a diffusion model producing probabilistic 15-day ensemble forecasts.
Status
- Claim
Result confirmed
- Our reporting
- High confidence
- Verification
- Peer-reviewed
- Importance
- 3 of 5
- Last verified
- 29 September 2026
Your AI and this story
- GPT-6 AstraIn its training data
- Claude Opus 5.5In its training data
- Gemini 4 HydrogenIn its training data
- Grok 4.7In its training data
It happened before the cutoffs of all four assistants, so it can be in their training data.
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
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.
Sources
2 sources from 2 sites. Numbers match the chips in the text.
2 sources: 2 primary
Primary
- Probabilistic weather forecasting with machine learning (Nature)nature.com, paper
- DeepMind: GenCastdeepmind.google, official
Changes
- Filed