# Neural general circulation models optimized to predict satellite-based precipitation observations

arXiv: https://arxiv.org/abs/2412.11973 · HTML: https://mlsanity.com/paper/2412.11973/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 14 references that are in the map.
- First posted on arXiv 16 Dec 2024.

## Built on by (most cited first, 6 of 6)

- [Generating realistic global precipitation fields from modelled atmospheric circulation](https://mlsanity.com/paper/2504.00307/) (2025)
- [The Equilibrium Response of Atmospheric Machine-Learning Models to Uniform Sea Surface Temperature Warming](https://mlsanity.com/paper/2510.02415/) (2025)
- [Advancing Seasonal Prediction of Tropical Cyclone Activity with a Hybrid AI-Physics Climate Model](https://mlsanity.com/paper/2505.01455/) (2025)
- [Investigating the use of terrain-following coordinates in AI-driven precipitation forecasts](https://mlsanity.com/paper/2503.00332/) (2025)
- [Hierarchical Testing of a Hybrid Machine Learning-Physics Global Atmosphere Model](https://mlsanity.com/paper/2602.11313/) (2026)
- [From Drops to Grid: Noise-Aware Spatio-Temporal Neural Process for Rainfall Estimation](https://mlsanity.com/paper/2605.05912/) (2026)

## Abstract (arXiv)

Climate models struggle to accurately simulate precipitation, particularly extremes and the diurnal cycle. Here, we present a hybrid model that is trained directly on satellite-based precipitation observations. Our model runs at 2.8^circ resolution and is built on the differentiable NeuralGCM framework. The model demonstrates significant improvements over existing general circulation models, the ERA5 reanalysis, and a global cloud-resolving model in simulating precipitation. Our approach yields reduced biases, a more realistic precipitation distribution, improved representation of extremes, and a more accurate diurnal cycle. Furthermore, it outperforms the mid-range precipitation forecast of the ECMWF ensemble. This advance paves the way for more reliable simulations of current climate and demonstrates how training on observations can be used to directly improve GCMs.