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AI-for-science approaches have been applied to solve scientific problems(e.g., nuclear fusion, ecology, genomics, meteorology) and have achieved highlypromising results. Spatial precipitation downscaling is one of the mostimportant meteorological problem and urgently requires the participation of AI.However, the lack of a well-organized and annotated large-scale dataset hindersthe training and verification of more effective and advancing deep-learningmodels for precipitation downscaling. To alleviate these obstacles, we presentthe first large-scale spatial precipitation downscaling dataset named RainNet,which contains more than $62,400$ pairs of high-quality low/high-resolutionprecipitation maps for over $17$ years, ready to help the evolution of deeplearning models in precipitation downscaling. Specifically, the precipitationmaps carefully collected in RainNet cover various meteorological phenomena(e.g., hurricane, squall), which is of great help to improve the modelgeneralization ability. In addition, the map pairs in RainNet are organized inthe form of image sequences ($720$ maps per month or 1 map/hour), showingcomplex physical properties, e.g., temporal misalignment, temporal sparse, andfluid properties. Furthermore, two deep-learning-oriented metrics arespecifically introduced to evaluate or verify the comprehensive performance ofthe trained model (e.g., prediction maps reconstruction accuracy). Toillustrate the applications of RainNet, 14 state-of-the-art models, includingdeep models and traditional approaches, are evaluated. To fully explorepotential downscaling solutions, we propose an implicit physical estimationbenchmark framework to learn the above characteristics. Extensive experimentsdemonstrate the value of RainNet in training and evaluating downscaling models.Our dataset is available at https://neuralchen.github.io/RainNet/.

RainNet: A Large-Scale Imagery Dataset and Benchmark for Spatial Precipitation Downscaling