  Extreme precipitation events, such as violent rainfall and hail storms,
routinely ravage economies and livelihoods around the developing world. Climate
change further aggravates this issue. Data-driven deep learning approaches
could widen the access to accurate multi-day forecasts, to mitigate against
such events. However, there is currently no benchmark dataset dedicated to the
study of global precipitation forecasts. In this paper, we introduce
\textbf{RainBench}, a new multi-modal benchmark dataset for data-driven
precipitation forecasting. It includes simulated satellite data, a selection of
relevant meteorological data from the ERA5 reanalysis product, and IMERG
precipitation data. We also release \textbf{PyRain}, a library to process large
precipitation datasets efficiently. We present an extensive analysis of our
novel dataset and establish baseline results for two benchmark medium-range
precipitation forecasting tasks. Finally, we discuss existing data-driven
weather forecasting methodologies and suggest future research avenues.
