MSWEP V3: Machine Learning-Powered Global Precipitation Estimates at 0.1° Hourly Resolution (1979–Present)

We introduce Version 3 (V3) of the gridded near real-time Multi-Source Weighted-Ensemble Precipitation (MSWEP) product—the first fully global, machine learning-powered precipitation (P) dataset, developed to meet the growing demand for timely and accurate P estimates amid escalating climate challenges. MSWEP V3 provides hourly data at 0.1° resolution from 1979 to the present, continuously updated with a latency of approximately two hours. Development follows a two-stage process. First, baseline P fields are generated using machine learning model stacks that integrate satellite- and (re)analysis-based P and air-temperature products, along with static variables. The models are trained using hourly and daily observations from 15,959 P gauges worldwide. Second, these baseline P fields are corrected using daily and monthly gauge observations from 57,666 and 86,000 stations globally, using a method that accounts for gauge proximity, reporting times, inter-gauge dependencies, and correlation lengths. To assess MSWEP V3's baseline performance, we evaluated 19 (quasi-) global gridded P products—including both uncorrected and gauge-based products—using observations from an independent set of 15,958 gauges excluded from the first training stage. The MSWEP V3 baseline achieved a median daily Kling-Gupta Efficiency (KGE) of 0.69, outperforming all evaluated products. Other uncorrected products achieved median KGE values of 0.61 (ERA5), 0.46 (IMERG-L V7), 0.38 (GSMaP V8), and 0.31 (CHIRP). Notably, the MSWEP V3 baseline also outperformed several gauge-based products, including IMERG-F V7 (0.62), CPC Unified (0.54), and CHIRPS (0.36). Using leave-one-out cross-validation, the daily gauge correction was found to improve the median daily correlation by 0.09, constrained by the already strong baseline performance. We anticipate that MSWEP V3 will substantially advance data-driven decision-making in hydrology and climate science, by enabling more reliable monitoring, forecasting, and management of water-related risks in a variable and changing climate.