# AI Weather & Climate Modeling: State of the Field (October 2026)

Research dossier · v1 · updated 2026-10-30 · 3,012 words · 45 sources · 30 claims · verification: verified

This dossier covers learned global medium-range weather models, probabilistic and ensemble systems, hybrid learned-physics climate components and kilometre-scale emulators as of October 2026. Its main takeaway is that data-driven global forecasts now match or beat operational baselines on most headline verification targets, while extremes, uncertainty and climate-scale reliability remain contested.

## Contents

- Executive summary
- Background and core concepts
- Timeline of key milestones
- State of the art
  - Deterministic global forecasts
  - Probabilistic and ensemble forecasting
  - Operational systems and industrial models
  - Hybrid and climate models
  - Kilometre-scale and regional emulation
  - Foundation models
  - Quantitative benchmarks
- Open problems and active debates
- Major programs and players
- Outlook: what to watch (November 2026 to April 2028)
- Key papers
- Glossary

## Sample findings

- Data-driven global medium-range models trained on reanalysis data, including GraphCast, now match or exceed the operational ECMWF deterministic forecast on most verification targets.
- GraphCast produces a 10-day global forecast at 0.25 degree resolution in under one minute, and reports better scores than the operational deterministic baseline on 90% of 1,380 verification targets.

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