# RainBench: Towards Global Precipitation Forecasting from Satellite Imagery

arXiv: https://arxiv.org/abs/2012.09670 · HTML: https://mlsanity.com/paper/2012.09670/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Dec 2020.

## Built on by (most cited first, 7 of 7)

- [Skillful Precipitation Nowcasting using Deep Generative Models of Radar](https://mlsanity.com/paper/2104.00954/) (2021)
- [Earthformer: Exploring Space-Time Transformers for Earth System Forecasting](https://mlsanity.com/paper/2207.05833/) (2022)
- [SwinRDM: Integrate SwinRNN with Diffusion Model towards High-Resolution and High-Quality Weather Forecasting](https://mlsanity.com/paper/2306.03110/) (2023)
- [IowaRain: A Statewide Rain Event Dataset Based on Weather Radars and Quantitative Precipitation Estimation](https://mlsanity.com/paper/2107.03432/) (2021)
- [WxC-Bench: A Novel Dataset for Weather and Climate Downstream Tasks](https://mlsanity.com/paper/2412.02780/) (2024)
- [DengueNet: Dengue Prediction using Spatiotemporal Satellite Imagery for Resource-Limited Countries](https://mlsanity.com/paper/2401.11114/) (2024)
- [DAMBench: A Multi-Modal Benchmark for Deep Learning-based Atmospheric Data Assimilation](https://mlsanity.com/paper/2511.01468/) (2025)

## Abstract (arXiv)

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 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 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.