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Benefit Ledger · verified · Climate & Environment

Pangu-Weather outperforms IFS on many medium-range scores at far lower cost

Huawei’s Pangu-Weather, published in Nature on 5 July 2023, reported 3D Earth-specific transformer forecasts that beat operational IFS on a majority of 3–7 day upper-air targets while running more than 10,000 times faster.

5 Jul 2023Tier 3 MajorMethodology 0.1

Current score

+1.73

10 base · Major (tier 3 of 5, 10 pts)
× 0.9000 attribution · Primary causal contribution
× 0.7000 evidence · Peer review or independent validation
× 0.5500 realization · Experimentally validated
× 0.5000 durability
Event-level product before credit split: 1.73

First Nature-level ML model to beat IFS on a broad medium-range scorecard (tier 3). High attribution. Peer review plus later independent MLWP comparisons. Research realization. Quickly surpassed on some scores.

What happened

The paper helped trigger the 2023–2025 wave of ML weather models. Forecasts are deterministic and trained on ERA5; operational caveats include precipitation and extreme-event calibration. ECMWF and others later ran independent intercomparisons.

Model attribution

+1.73

Pangu

3D Earth-specific transformer weather model.

The published model is the forecast system.

Attribution 0.9000 · Credit share 100% · Huawei

Claims

  • Pangu-Weather outperformed IFS on many 3–7 day deterministic scores in the Nature evaluation.

    outcome · supported

Sources

primary sources

Secondary domains: Computer Science

Revision history

  • 13 Sep 2026 · 0.00 → 1.73

    Initial adjudicated seed score under methodology 0.1.

Pangu-Weather outperforms IFS on many medium-range scores at far lower cost · NetGoodIndex