Articles | Volume 14, issue 10
https://doi.org/10.5194/gmd-14-6355-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-6355-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
22 Oct 2021
Development and technical paper |
| 22 Oct 2021
| 22 Oct 2021
Fast and accurate learned multiresolution dynamical downscaling for precipitation
Jiali Wang
Environmental Science Division, Argonne National Laboratory, Lemont, IL, USA
Data Science and Learning Division, Argonne National Laboratory, Lemont, IL, USA
Ian Foster
Data Science and Learning Division, Argonne National Laboratory, Lemont, IL, USA
Won Chang
Division of Statistics and Data Science, University of Cincinnati, Cincinnati, OH, USA
Rajkumar Kettimuthu
Data Science and Learning Division, Argonne National Laboratory, Lemont, IL, USA
Environmental Science Division, Argonne National Laboratory, Lemont, IL, USA
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- Super-resolution of three-dimensional temperature and velocity for building-resolving urban micrometeorology using physics-guided convolutional neural networks with image inpainting techniques Y. Yasuda et al. https://doi.org/10.1016/j.buildenv.2023.110613
- Wasserstein GAN-based precipitation downscaling with optimal transport for enhancing perceptual realism K. Shiraishi et al. https://doi.org/10.1186/s40645-026-00815-w
- Novel Parameter Calibration Method of WRF-Hydro in Ungauged Areas Combining Satellite-Based Soil Moisture and Multisource Meteorological Data Q. Zhao et al. https://doi.org/10.1007/s11269-025-04336-x
- Bridging Global Climate Solutions and Local Realities: Evaluating Neural Networks for High-Resolution Downscaling D. Taniushkina et al. https://doi.org/10.1109/ACCESS.2026.3673693
- Modelling of Deep Learning-Based Downscaling for Wave Forecasting in Coastal Area D. Adytia et al. https://doi.org/10.3390/w15010204
- PSRGAN: Generative Adversarial Networks for Precipitation Downscaling Z. Li et al. https://doi.org/10.1109/LGRS.2025.3528018
- On the suitability of a convolutional neural network based RCM-emulator for fine spatio-temporal precipitation A. Doury et al. https://doi.org/10.1007/s00382-024-07350-8
- Efficient Super‐Resolution of Near‐Surface Climate Modeling Using the Fourier Neural Operator P. Jiang et al. https://doi.org/10.1029/2023MS003800
- Successful Precipitation Downscaling Through an Innovative Transformer-Based Model F. Yang et al. https://doi.org/10.3390/rs16224292
- Projected Changes in Extreme Wet and Dry Conditions in Greece E. Kostopoulou & C. Giannakopoulos https://doi.org/10.3390/cli11030049
- A two-stage model for spatial downscaling of daily precipitation data W. Lei et al. https://doi.org/10.1007/s00371-023-03236-8
- Using Machine Learning to Cut the Cost of Dynamical Downscaling S. Hobeichi et al. https://doi.org/10.1029/2022EF003291
- Generative deep learning for data generation in natural hazard analysis: motivations, advances, challenges, and opportunities Z. Ma et al. https://doi.org/10.1007/s10462-024-10764-9
- Deep learning approach for downscaling the significant wave height based on CBAM_CGAN M. Yu et al. https://doi.org/10.1016/j.oceaneng.2024.119169
- Downscaling atmospheric chemistry simulations with physically consistent deep learning A. Geiss et al. https://doi.org/10.5194/gmd-15-6677-2022
- Can AI be enabled to perform dynamical downscaling? A latent diffusion model to mimic kilometer-scale COSMO5.0_CLM9 simulations E. Tomasi et al. https://doi.org/10.5194/gmd-18-2051-2025
- Statistical Downscaling of SEVIRI Land Surface Temperature to WRF Near-Surface Air Temperature Using a Deep Learning Model A. Afshari et al. https://doi.org/10.3390/rs15184447
- Spatio‐Temporal Super‐Resolution Data Assimilation (SRDA) Utilizing Deep Neural Networks With Domain Generalization Y. Yasuda & R. Onishi https://doi.org/10.1029/2023MS003658
- WGAN-GP-Based Conditional GAN (cGAN) With Extreme Critic for Precipitation Downscaling in a Key Agricultural Region of the Northeastern U.S. J. Lee & S. Park https://doi.org/10.1109/ACCESS.2025.3549443
- Rotationally equivariant super-resolution of velocity fields in two-dimensional flows using convolutional neural networks Y. Yasuda & R. Onishi https://doi.org/10.1063/5.0132326
- A Transfer Learning-Enhanced Generative Adversarial Network for Downscaling Sea Surface Height through Heterogeneous Data Fusion Q. Zhang et al. https://doi.org/10.3390/rs16050763
- Domain-knowledge-guided deep learning for probabilistic precipitation downscaling over southeastern Tibetan Plateau D. Li et al. https://doi.org/10.1016/j.jhydrol.2026.136242
- A Fast Generative Adversarial Network Combined With Transformer for Downscaling GRACE Terrestrial Water Storage Data in Southwestern China S. Gu et al. https://doi.org/10.1109/TGRS.2024.3349548
- Efficient precipitation downscaling over the Southern Tibetan Plateau with deep learning surrogates D. Li et al. https://doi.org/10.1016/j.atmosres.2026.109079
- Learn from Simulations, Adapt to Observations: Super-Resolution of Isoprene Emissions via Unpaired Domain Adaptation A. Giganti et al. https://doi.org/10.3390/rs16213963
- Efficient Kilometer‐Scale Precipitation Downscaling With Conditional Wavelet Diffusion C. Yi et al. https://doi.org/10.1029/2025JH000941
- On the limitations of deep learning for statistical downscaling of climate change projections: The transferability and the extrapolation issues A. Hernanz et al. https://doi.org/10.1002/asl.1195
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- Comparison of machine learning statistical downscaling and regional climate models for temperature, precipitation, wind speed, humidity and radiation over Europe under present conditions A. Hernanz et al. https://doi.org/10.1002/joc.8190
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Saved (final revised paper)
Latest update: 30 Aug 2026
Short summary
Downscaling, the process of generating a higher spatial or time dataset from a coarser observational or model dataset, is a widely used technique. Two common methodologies for performing downscaling are to use either dynamic (physics-based) or statistical (empirical). Here we develop a novel methodology, using a conditional generative adversarial network (CGAN), to perform the downscaling of a model's precipitation forecasts and describe the advantages of this method compared to the others.
Downscaling, the process of generating a higher spatial or time dataset from a coarser...
