  AI-for-science approaches have been applied to solve scientific problems
(e.g., nuclear fusion, ecology, genomics, meteorology) and have achieved highly
promising results. Spatial precipitation downscaling is one of the most
important meteorological problem and urgently requires the participation of AI.
However, the lack of a well-organized and annotated large-scale dataset hinders
the training and verification of more effective and advancing deep-learning
models for precipitation downscaling. To alleviate these obstacles, we present
the first large-scale spatial precipitation downscaling dataset named RainNet,
which contains more than $62,400$ pairs of high-quality low/high-resolution
precipitation maps for over $17$ years, ready to help the evolution of deep
learning models in precipitation downscaling. Specifically, the precipitation
maps carefully collected in RainNet cover various meteorological phenomena
(e.g., hurricane, squall), which is of great help to improve the model
generalization ability. In addition, the map pairs in RainNet are organized in
the form of image sequences ($720$ maps per month or 1 map/hour), showing
complex physical properties, e.g., temporal misalignment, temporal sparse, and
fluid properties. Furthermore, two deep-learning-oriented metrics are
specifically introduced to evaluate or verify the comprehensive performance of
the trained model (e.g., prediction maps reconstruction accuracy). To
illustrate the applications of RainNet, 14 state-of-the-art models, including
deep models and traditional approaches, are evaluated. To fully explore
potential downscaling solutions, we propose an implicit physical estimation
benchmark framework to learn the above characteristics. Extensive experiments
demonstrate the value of RainNet in training and evaluating downscaling models.
Our dataset is available at https://neuralchen.github.io/RainNet/.
