Fusion of multi-source precipitation records via coordinate-based generative model

Abstract

Precipitation remains one of the most challenging climate variables to observe and predict accurately. Existing datasets face intricate trade-offs: gauge observations are relatively trustworthy but sparse, satellites provide global coverage with retrieval uncertainties, and numerical models offer physical consistency but are biased and computationally intensive. Here we introduce PRIMER (Precipitation Record Infinite MERging), a deep generative framework that fuses these complementary sources to produce accurate, high-resolution, full-coverage precipitation estimates. PRIMER employs a coordinate-based diffusion model that learns from arbitrary spatial locations and associated precipitation values, enabling seamless integration of gridded data and irregular gauge observations. Through two-stage training--first learning large-scale patterns, then refining with accurate gauge measurements--PRIMER captures both large-scale climatology and local precision. Once trained, it can downscale forecasts, interpolate sparse observations, and correct systematic biases within a principled Bayesian framework. Using gauge observations as ground truth, PRIMER effectively corrects biases in existing datasets, yielding statistically significant error reductions at most stations and furthermore enhancing the spatial coherence of precipitation fields. Crucially, it generalizes without retraining, correcting biases in operational forecasts it has never seen. This demonstrates how generative AI can transform Earth system science by combining imperfect data, providing a scalable solution for global precipitation monitoring and prediction.