---
title: 'GraphCast: Learning skillful medium-range global weather forecasting'
license: CC-BY-4.0
license_url: https://creativecommons.org/licenses/by/4.0/
license_note: 'paper is CC BY 4.0 (verified on arXiv abs page 2026-11-11; icon by-4.0.png, http://creativecommons.org/licenses/by/4.0/); note is our own summary'
source: https://iinventedlight.com/library/notes/2212-12794-graphcast.md
source_license: 'CC BY 4.0'
attribution: 'Summary by iinventedlight (AI-assisted). Source: Remi Lam, Alvaro Sanchez-Gonzalez, Matthew Willson, and 15 coauthors, GraphCast: Learning skillful medium-range global weather forecasting (CC BY 4.0).'
author: 'iinventedlight (AI-assisted)'
date: 2026-11-11
status: published
approved_by: "Cole Withington, 2026-11-11 20:58 PT, Grok Bot chat widget t1717s7407: \"Publish all 100 including the LLMs Encode Clinical Knowledge note\""
checked_on: 2026-11-11
as_of: 2026-11-11
confidence: medium
untested: 'Not reproduced. Abs page and HTML key sections read 2026-11-11. Named code URLs checked for HTTP 200; code not run.'
category: arxiv
topic_area: 'ai-research: AI-for-science'
tags: arxiv, weather, graph-neural-networks, ai-for-science
arxiv_id: 2212.12794
alias: 2212.12794-graphcast
sources:
  - id: S1
    title: 'GraphCast: Learning skillful medium-range global weather forecasting'
    publisher: arXiv
    authors: 'Remi Lam, Alvaro Sanchez-Gonzalez, Matthew Willson, and 15 coauthors (listed on the arXiv abs page)'
    url: https://arxiv.org/abs/2212.12794
    version_read: 'v2 (4 Aug 2023)'
    accessed: 2026-11-11
    license: 'CC BY 4.0'
---
# GraphCast: Learning skillful medium-range global weather forecasting

## Summary
GraphCast is a machine-learning weather model trained from reanalysis data. It predicts hundreds of weather variables, over 10 days at 0.25 degree resolution globally, in under one minute. The authors show that GraphCast significantly outperforms the most accurate operational deterministic systems on 90% of 1380 verification targets, and that its forecasts help with severe events including tropical cyclones, atmospheric rivers, and extreme temperatures. The method uses graph neural networks and is trained on ECMWF ERA5 reanalysis. Code and trained weights are pointed to a public GitHub repository. [S1]

## Claims
- id: C1
  claim: 'GraphCast predicts hundreds of weather variables over 10 days at 0.25 degree resolution, in under one minute.'
  source: S1
  evidence: "over 10 days at 0.25 degree resolution globally, in under one minute"
- id: C2
  claim: 'It outperforms the leading operational deterministic systems on 90% of 1380 verification targets.'
  source: S1
  evidence: "significantly outperforms the most accurate operational deterministic systems on 90% of 1380 verification targets"
- id: C3
  claim: 'Code and trained weights are pointed to a public GitHub repository.'
  source: S1
  evidence: "https://github.com/deepmind/graphcast"

## Verification
- Code available: Paper: https://github.com/deepmind/graphcast, which returned HTTP 200 and redirected to https://github.com/google-deepmind/weathernext on 2026-11-11. Not run.
- Reproduced: not reproduced (2026-11-11). Code not run.
- Related work: No live library note is a paper-verification page for GraphCast itself. A pointer note in this week's batch covers the Fourier Neural Operator.
- Limits: The comparison is against deterministic HRES, not a full ensemble. The authors discuss regions where HRES remains stronger.
- License: CC BY 4.0 on the abs page (2026-11-11; icon by-4.0.png; http://creativecommons.org/licenses/by/4.0/). Submitted 24 Dec 2022 (v1); last revised 4 Aug 2023 (v2). Sources: [S1] https://arxiv.org/abs/2212.12794, v2 (4 Aug 2023). checked_on: 2026-11-11.

## When to apply
Use when asking how a learned global model compared with operational numerical weather prediction on medium-range forecasts.

## Facts
- id: F1
  claim: 'GraphCast predicts hundreds of weather variables, over 10 days at 0.25 degree resolution globally, in under one minute.'
  confidence: medium
  date: 2026-11-11
  sources: [S1]
  license: CC BY 4.0
- id: F2
  claim: 'The authors report that GraphCast outperforms the most accurate operational deterministic systems on 90% of 1380 verification targets.'
  confidence: medium
  date: 2026-11-11
  sources: [S1]
  license: CC BY 4.0
- id: F3
  claim: 'Code and trained weights were released at the GitHub URL given in the paper.'
  confidence: medium
  date: 2026-11-11
  sources: [S1]
  license: CC BY 4.0

## Sources
- [S1] arXiv 2212.12794, https://arxiv.org/abs/2212.12794. License: CC BY 4.0. v2 (4 Aug 2023). Accessed 2026-11-11.

License: this note is CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Summary by iinventedlight (AI-assisted). Paper is CC BY 4.0.

---

Agents: [/agents.md](/agents.md) | [/.well-known/agent-library.json](/.well-known/agent-library.json).