Precipitation Downscaling

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3 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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RainAtlas: A Multi-Continental Dataset for Precipitation Downscaling
Sep 30, 2026Pierre-Louis Lemaire, Luca Schmidt, Wietze Suijker +2Precipitation Downscaling Cross-Domain Generalization
connAIx Research School, Tübingen AI Center, University of Tübingen · Mila - Quebec AI Institute · Cluster of Excellence Machine Learning, University of Tübingen +3
Lightweight Probabilistic Downscaling from a Deterministic Base Model
Sep 24, 2026Joseph McLean, Tiffany Vlaar, Sigrid Passano Hellan +1Climate Science U-Net
University of Glasgow · Climate System and Climate Services, NORCE Research AS and Bjerknes Centre for Climate Research, Bergen, Norway
Evaluating Cross-region Generalization for Wavelet-Diffusion Precipitation Downscaling
Sep 23, 2026Weikang Qian, Yixin Wen, Chugang Yi +3Spatial Generalization Precipitation Downscaling
University of Florida, Department of Geography, Gainesville, FL, USA · School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, NY, USA · Institute for Advanced Computational Science, Stony Brook University, NY, USA +5
Diffusion-Based Refinement for Kilometer-Scale Probabilistic Precipitation Nowcasting
Aug 31, 2026Dohyun Park, Changhoon Song, Tengyuan Chang +2Precipitation Forecasting Probabilistic Forecasting
Department of Mathematical Sciences, Seoul National University, Seoul, Republic of Korea. · Research Institute of Mathematics, Seoul National University, Seoul,2026 Republic of Korea. · Department of Environmental Management, Seoul National University, Seoul, Republic of Korea.
Regional Climate Model Emulation with Diffusion Approaches: What is the Added Value of Generative Machine Learning?
Jun 12, 2026Mikel N. Legasa, Antoine Doury, Achille Gellens +4Surrogate Modeling Precipitation Downscaling
Laboratoire des Sciences du Climat et de l’Environnement (LSCE), CEA/CNRS/UVSQ, Universit´e Paris Saclay, UMR8212, 91191 Gif-sur-Yvette, France · CNRM, Universit´e de Toulouse, M´et´eo-France, CNRS, Toulouse, France · Institut Pierre-Simon Laplace (IPSL), FR636, Paris, France +1
Flow Matching for Convective-Scale Precipitation Downscaling
May 29, 2026Tom WetherellFlow Matching Conditional Flow Matching
Met Office
Longwang: Zero-Shot Global Spatiotemporal Precipitation Downscaling with a Latent Generative Prior
May 17, 2026Yue Wang, Daniele VisioniZero-Shot Learning Climate Science
1*Department of Earth and Atmospheric Sciences, Cornell University, 112 Hollister Drive, Ithaca, 14853, NY, USA.
SwAIther-Precip: Lead-Time-Aware Bias Correction Enables Kilometer-Scale Downscaling of Global AI Precipitation Forecasts over Switzerland
May 15, 2026Dan Assouline, Erwan Koch, Federico Amato +5Precipitation Forecasting Time Series Forecasting
Swiss Data Science Center, ETH, Zürich, Switzerland · Expertise Center for Climate Extremes, University of Lausanne, Lausanne, Switzerland · Faculty of Geosciences and Environment, University of Lausanne, Lausanne, Switzerland +1
Multi-Quantile Regression for Extreme Precipitation Downscaling
May 12, 2026Hamed Najafi, Gareth Lagerwall, Jayantha Obeysekera +1Quantile Regression Precipitation Downscaling
Florida International University Miami, FL · Everglade foundation Miami, FL
Conditional Flow Matching for Probabilistic Downscaling of Maximum 3-day Snowfall in Alaska
Apr 28, 2026Douglas Brinkerhoff, Elizabeth FischerUncertainty Quantification Climate Science
Department of Computer Science, University of Montana · International Arctic Research Center, University of Alaska

Sep 30, 2026, cs.LGJ/K move · Enter open · S save

RainAtlas: A Multi-Continental Dataset for Precipitation Downscaling

Pierre-Louis Lemaire, Luca Schmidt, Wietze Suijker, Alex Hernandez-Garcia+1

connAIx Research School, Tübingen AI Center, University of Tübingen · Mila - Quebec AI Institute · Cluster of Excellence Machine Learning, University of Tübingen+3

Precipitation Downscaling Cross-Domain Generalization Climate Downscaling

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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.

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