43 citations as recorded by crossref.

1. Improving the statistical downscaling performance of climatic parameters with convolutional neural networks A. Hosseini Baghanam et al. https://doi.org/10.2166/wcc.2024.592
2. Emulating aerosol optics with randomly generated neural networks A. Geiss et al. https://doi.org/10.5194/gmd-16-2355-2023
3. 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
4. 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
5. 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
6. 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
7. Modelling of Deep Learning-Based Downscaling for Wave Forecasting in Coastal Area D. Adytia et al. https://doi.org/10.3390/w15010204
8. PSRGAN: Generative Adversarial Networks for Precipitation Downscaling Z. Li et al. https://doi.org/10.1109/LGRS.2025.3528018
9. 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
10. Efficient Super‐Resolution of Near‐Surface Climate Modeling Using the Fourier Neural Operator P. Jiang et al. https://doi.org/10.1029/2023MS003800
11. Successful Precipitation Downscaling Through an Innovative Transformer-Based Model F. Yang et al. https://doi.org/10.3390/rs16224292
12. Projected Changes in Extreme Wet and Dry Conditions in Greece E. Kostopoulou & C. Giannakopoulos https://doi.org/10.3390/cli11030049
13. A two-stage model for spatial downscaling of daily precipitation data W. Lei et al. https://doi.org/10.1007/s00371-023-03236-8
14. Using Machine Learning to Cut the Cost of Dynamical Downscaling S. Hobeichi et al. https://doi.org/10.1029/2022EF003291
15. 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
16. 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
17. Downscaling atmospheric chemistry simulations with physically consistent deep learning A. Geiss et al. https://doi.org/10.5194/gmd-15-6677-2022
18. 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
19. 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
20. Spatio‐Temporal Super‐Resolution Data Assimilation (SRDA) Utilizing Deep Neural Networks With Domain Generalization Y. Yasuda & R. Onishi https://doi.org/10.1029/2023MS003658
21. 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
22. 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
23. 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
24. 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
25. 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
26. 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
27. Efficient Kilometer‐Scale Precipitation Downscaling With Conditional Wavelet Diffusion C. Yi et al. https://doi.org/10.1029/2025JH000941
28. 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
29. Regional climate model emulator based on deep learning: concept and first evaluation of a novel hybrid downscaling approach A. Doury et al. https://doi.org/10.1007/s00382-022-06343-9
30. Medium-term forecasting of global horizontal solar radiation in Brazil using machine learning-based methods A. Weyll et al. https://doi.org/10.1016/j.energy.2024.131549
31. Four-Dimensional Super-Resolution Data Assimilation (4D-SRDA) for Prediction of Three-Dimensional Quasi-Geostrophic Flows A. Notsu et al. https://doi.org/10.2151/sola.21B-001
32. Interpretable deep learning method integrating spatial self-attention for generating bias-corrected high-resolution GFS precipitation forecasts H. Wang et al. https://doi.org/10.1016/j.atmosres.2026.108832
33. Probabilistic Super-Resolution for Urban Micrometeorology via a Schrödinger Bridge Y. Yasuda & R. Onishi https://doi.org/10.1007/s44393-025-00007-7
34. A review of future weather data for assessing climate change impacts on buildings and energy systems Z. Zeng et al. https://doi.org/10.1016/j.rser.2024.115213
35. A multi-resolution Feature fusion network for significant wave height downscaling Y. Wang et al. https://doi.org/10.1016/j.oceaneng.2025.123401
36. UPD-Diff: a unified precipitation downscaling method based on multi-stream elucidating diffusion model J. Chen et al. https://doi.org/10.1088/2632-2153/ae4da6
37. Two-stage super-resolution simulation method of three-dimensional street-scale atmospheric flows for real-time urban micrometeorology prediction Y. Yasuda & R. Onishi https://doi.org/10.1016/j.uclim.2025.102300
38. Assimilation of surface soil moisture jointly retrieved by multiple microwave satellites into the WRF-Hydro model in ungauged regions: Towards a robust flood simulation and forecasting L. Chao et al. https://doi.org/10.1016/j.envsoft.2022.105421
39. An illustration of model agnostic explainability methods applied to environmental data C. Wikle et al. https://doi.org/10.1002/env.2772
40. 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
41. Leveraging Land Cover Priors for Isoprene Emission Super-Resolution C. Ummerle et al. https://doi.org/10.3390/rs17101715
42. Using Explainability to Inform Statistical Downscaling Based on Deep Learning Beyond Standard Validation Approaches J. González‐Abad et al. https://doi.org/10.1029/2023MS003641
43. Street-level temperature estimation using graph neural networks: Performance, feature embedding and interpretability Y. Yu et al. https://doi.org/10.1016/j.uclim.2024.102003