NeuralGCM harnesses AI to better simulate long-range global precipitation

Precipitation remains one of the trickiest tasks for global-scale weather and climate models. That’s because exactly where, when and how much precipitation will fall depends on a series of events happening at scales that are typically below the model resolution. Simulating precipitation is especially challenging for extreme events and over long periods of time. Whether it’s farmers knowing which day to plant seeds to optimize their harvest, or city planners knowing how to prepare for a 100-year storm, precipitation forecasts are some of the most relevant for humans.

Last year, we introduced our open-sourced hybrid atmospheric model NeuralGCM, which combines machine learning (ML) and physics to run fast, efficient and accurate global atmospheric simulations. In the 2024 paper, NeuralGCM generated more accurate 2–15 day weather forecasts and reproduced historical temperatures over four decades with greater precision than traditional atmospheric models, marking a significant step towards developing more accessible climate models.

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.

NeuralGCM is part of a broader effort to advance weather and climate science, within Earth AI. As a hybrid model that combines physics and AI to tackle longer-range questions, it complements other AI-only weather models like the recently introduced WeatherNext 2 update from our Google colleagues.