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

Precipitation remains one of the most challenging climate variables to observe and predict. Existing datasets face intricate trade-offs: gauges are relatively trustworthy but sparse, satellites provide near-global coverage with retrieval uncertainties, and numerical models offer physical consistency but are biased. Here we introduce PRIMER (Precipitation Records Infinite MERging), a framework that fuses these complementary sources. PRIMER employs a coordinate-based diffusion model that learns from arbitrary spatial locations and associated intensity values, enabling seamless integration of gridded data and irregular gauge observations. Through two-stage training-first learning large-scale patterns, then refining with gauge measurements-PRIMER captures both large-scale structure and local precision. Once trained, it can correct biases in existing datasets-yielding significant error reductions at most gauge sites-and downscale reanalysis. In addition, by combining background estimates with extra gauges, it produces analysis fields that further reduce errors. All tasks are achieved through posterior sampling utilizing the prior obtained by fusing multi-source records. Crucially, it generalizes without retraining, correcting biases in operational forecasts and downscaling future scenario precipitation fields. This demonstrates how PRIMER can transform imperfect data into a source of strength.