# Google Research — NeuralGCM harnesses AI to better simulate long-range global precipitation

- Company: Google Research (research.google)
- Announced: 2026-01-12T17:52:00+00:00
- Category: model-update
- Subject: Research
- Models affected: NeuralGCM
- Source: https://research.google/blog/neuralgcm-harnesses-ai-to-better-simulate-long-range-global-precipitation/
- Record: https://forck.live/items/1380-neuralgcm-harnesses-ai-to-better-simulate-long-range-global-precipitation

Google Research describes an update to NeuralGCM, a hybrid atmospheric model that combines physics-based modeling with a neural network. The new version is trained directly on NASA satellite-based precipitation observations (2001–2018) instead of reanalysis data, leading to improved simulations of average precipitation, precipitation extremes (especially the top 0.1% of rainfall), and the daily weather cycle. At 280 km resolution, it outperforms a leading operational model from ECMWF on 2–15 day forecasts and shows improvements for multi-decadal climate simulations.

## Evidence

Verbatim from https://research.google/blog/neuralgcm-harnesses-ai-to-better-simulate-long-range-global-precipitation/:

> Now in “Neural general circulation models for modeling precipitation”, published in Science Advances, we describe how NeuralGCM was trained on satellite-based precipitation observations to achieve improved simulations of precipitation. Notably, at the current resolution of 280 km, we see improvements against a leading operational model for medium-range weather forecasting (up to 15 days) and against atmospheric models used for multi-decadal climate simulations. We find NeuralGCM more accurately reproduces average precipitation, precipitation extremes — with major improvements for the top 0.1% of rainfall — and the daily weather cycle.

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Record: https://forck.live/items/1380-neuralgcm-harnesses-ai-to-better-simulate-long-range-global-precipitation
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