1,2,1,1- 1Institute of Meteorology and Climate Research - Atmospheric Environmental Research (IMK-IFU), Campus Alpin, Karlsruhe Institute of Technology, Garmisch-Partenkirchen, Germany
- 2Institute of Meteorology and Climate Research - Atmospheric Trace Gases and Remote Sensing (IMK-ASF), Karlsruhe Institute of Technology, Karlsruhe, Germany
- 3Institute of Geography, University of Augsburg, Augsburg, Germany
Global reanalysis products such as ERA5 are indispensable for climate and hydrological studies, yet their coarse spatial and temporal resolution limits the representation of localised and short-lived precipitation extremes. Building on our earlier work [1], we now present the published and ready-to-use version of spateGAN-ERA5, a generative AI framework for global spatio-temporal downscaling of ERA5 precipitation to kilometre and sub-hourly scales (2 km, 10 min) [2].
The model, trained using gauge-adjusted radar observations over Germany, generates realistic high-resolution precipitation ensembles conditioned on ERA5 inputs. We demonstrate robust performance across multiple climate regimes through independent evaluations over Germany, the United States, and Australia, showing clear improvements in spatial structure, temporal coherence, and extreme rainfall representation compared to native ERA5 fields. Ensemble generation further enables probabilistic uncertainty quantification.
To facilitate broad adoption, we provide a public, easy-to-use downscaling tool [3] that enables on-demand generation of high-resolution precipitation for any region and time period worldwide. The approach is computationally efficient and applicable on modest GPU hardware, making it suitable for both regional studies and large-scale applications. spateGAN-ERA5 thus establishes a practical pathway toward global high-resolution precipitation products for climate impact analysis, hydrological modelling, and AI-based weather and climate research.
[1] Glawion, L., Polz, J., Kunstmann, H., Fersch, B., & Chwala, C. (2023). spateGAN: Spatio‑temporal downscaling of rainfall fields using a cGAN approach. Earth and Space Science, 10, e2023EA002906. https://doi.org/10.1029/2023EA002906
[2] Glawion, L., Polz, J., Kunstmann, H., Fersch, B., & Chwala, C. (2025). Global spatio‑temporal ERA5 precipitation downscaling to km and sub‑hourly scale using generative AI. npj Climate and Atmospheric Science, 8, 219. https://doi.org/10.1038/s41612-025-01103-y
[3] https://github.com/LGlawion/spateGAN_ERA5
How to cite: Glawion, L., Polz, J., Kunstmann, H., Fersch, B., and Chwala, C.: From ERA5 to Precipitation Extremes: Global km-Scale, Sub-Hourly Downscaling with Generative AI, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17479, https://doi.org/10.5194/egusphere-egu26-17479, 2026.
