Summary
More intense and frequent heavy rainfall is happening because of climate change, making it important to predict rain at a fine scale for local areas. The paper discusses RainAtlas, a large dataset that combines detailed hourly rain data from three different continents on a common grid. This makes it easier to train and test machine learning models for improving rain forecasts at about 2 kilometers resolution, even in places without many observations. The authors found that models trained in one region don’t always work well in others, highlighting the importance of testing across multiple regions. RainAtlas provides a standardized way to study and improve rain downscaling methods worldwide.
What this means in practice
- •For weather forecasters: Enhance local rainfall predictions by using RainAtlas to train models with standardized multi-continental rain data at fine spatial scale.
- •For water resource managers: Integrate multi-region precipitation forecasts from RainAtlas-trained models to better plan flood control and water infrastructure in under-sampled areas.
Authors
Pierre-Louis Lemaire, Luca Schmidt, Wietze Suijker, Alex Hernandez-Garcia, David Rolnick
Abstract
Extreme rainfall events are increasing in intensity and frequency as climate change accelerates. While kilometer-scale precipitation forecasts are critical for supporting local decision-making, the limited availability of high-resolution precipitation observations hinders their accuracy, especially in under-resourced regions. Machine learning models are widely used to downscale precipitation data to km-scale, but their application to unseen geographies presents challenges. First, processing raw high-resolution precipitation datasets across regions requires significant engineering and domain expertise. Second, generalization across regions remains difficult. To help overcome these barriers, we release RainAtlas, a large-scale, ML-ready and multi-continental dataset for precipitation downscaling. Covering three continents, RainAtlas harmonizes heterogeneous hourly km-scale observations to a common 2-km grid. Each regional partition contains around 210,000 aligned low- and high-resolution precipitation pairs, respectively from ERA5 reanalysis and direct observations. We benchmark state-of-the-art ML-based downscaling models across RainAtlas using a wide range of metrics. Our evaluation reveals substantial variance in out-of-domain generalization depending on the training regions. This underscores the need for cross-regional, multi-source km-scale evaluation, establishing RainAtlas as a well-positioned benchmark for precipitation downscaling research.