139 citations as recorded by crossref.

1. Convolutional Graph Neural Network with Novel Loss Strategies for Daily Temperature and Precipitation Statistical Downscaling over South China W. Yan et al. 10.1007/s00376-024-3347-z
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3. Projected climatic drought events in drought-prone cities: Insights from high-resolution downscaled CMIP6 data X. Li et al. 10.1016/j.cliser.2024.100488
4. Dispersion Simulation Using the 1-km Gridded Wind Fields Constructed by Super-Resolution Surrogate Downscaling T. SEKIYAMA & M. KAJINO 10.2151/jmsj.2025-016
5. High-Resolution Deep-Learning and Dynamical Climate Downscaling for Impact Modeling in Southeast South America M. Bettolli et al. 10.1007/s41748-025-00661-8
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8. Spatial Downscaling of Satellite Sea Surface Wind with Soft-Sharing Multi-Task Learning Y. Yue et al. 10.3390/rs17040587
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26. Future projections of precipitation extremes over East Asia based on a deep learning downscaled CMIP6 high-resolution (0.1°) dataset Y. Yang et al. 10.1007/s10584-024-03844-w
27. Downscaling CORDEX Through Deep Learning to Daily 1 km Multivariate Ensemble in Complex Terrain D. Quesada‐Chacón et al. 10.1029/2023EF003531
28. Deep-learning-based sub-seasonal precipitation and streamflow ensemble forecasting over the source region of the Yangtze River N. Dong et al. 10.5194/hess-29-2023-2025
29. Statistical downscaling of daily precipitation over southeastern South America: Assessing the performance in extreme events M. Olmo & M. Bettolli 10.1002/joc.7303
30. Predictor Selection for CNN-based Statistical Downscaling of Monthly Precipitation D. Yang et al. 10.1007/s00376-022-2119-x
31. Deep Learning Downscaling of Precipitation Projection over Central Asia Y. Jiang et al. 10.3390/w17071089
32. Monthly 0.05° winter months snow depth dataset for the Northern Hemisphere from 21 CMIP6 models S. Lin et al. 10.1038/s41597-025-04925-w
33. Using Explainability to Inform Statistical Downscaling Based on Deep Learning Beyond Standard Validation Approaches J. González‐Abad et al. 10.1029/2023MS003641
34. Multi-Level Circulation Pattern Classification Based on the Transfer Learning CNN Network Y. Liu et al. 10.3390/atmos13111861
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40. Comparative Assessment of Image Super-Resolution Techniques for Spatial Downscaling of Gridded Rainfall Data S. Golla et al. 10.1007/s42979-024-02653-3
41. Multi‐objective downscaling of precipitation time series by genetic programming T. Zerenner et al. 10.1002/joc.7172
42. A new statistical downscaling approach for short‐term forecasting of summer air temperatures through a fusion of deep learning and spatial interpolation D. Cho et al. 10.1002/qj.4643
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45. Triple coupling random forest approach for bias correction of ensemble precipitation data derived from Earth system models for Divandareh‐Bijar Basin (Western Iran) F. Zebarjadian et al. 10.1002/joc.8458
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47. Using a two-step downscaling method to assess the impact of climate change on total nitrogen load in a small basin X. Li et al. 10.1016/j.jhydrol.2023.130510
48. MAUNet: a max-average neural network architecture for precipitation downscaling S. Mishra Sharma & A. Mitra 10.1007/s00521-024-10012-9
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50. On deep learning-based bias correction and downscaling of multiple climate models simulations F. Wang & D. Tian 10.1007/s00382-022-06277-2
51. High-resolution downscaling with interpretable deep learning: Rainfall extremes over New Zealand N. Rampal et al. 10.1016/j.wace.2022.100525
52. LSTM and Transformer-based framework for bias correction of ERA5 hourly wind speeds F. Houndekindo & T. Ouarda 10.1016/j.energy.2025.136498
53. Deep-learning post-processing of short-term station precipitation based on NWP forecasts Q. Liu et al. 10.1016/j.atmosres.2023.107032
54. On the limitations of deep learning for statistical downscaling of climate change projections: The transferability and the extrapolation issues A. Hernanz et al. 10.1002/asl.1195
55. Artificial intelligence-driven precipitation downscaling and projections over Thailand using CMIP6 climate models M. Waqas & U. Humphries 10.1080/20964471.2025.2547500
56. Estimating pros and cons of statistical downscaling based on EQM bias adjustment as a complementary method to dynamical downscaling A. Reder et al. 10.1038/s41598-024-84527-5
57. Improving Subseasonal‐To‐Seasonal Prediction of Summer Extreme Precipitation Over Southern China Based on a Deep Learning Method Y. Lyu et al. 10.1029/2023GL106245
58. Deep learning in statistical downscaling for deriving high spatial resolution gridded meteorological data: A systematic review Y. Sun et al. 10.1016/j.isprsjprs.2023.12.011
59. A two-step downscaling method for high-scale super-resolution of daily temperature — a case study of Wei River Basin, China X. Li et al. 10.1007/s11356-022-24422-6
60. Impact of Climate Change on Wind Power Generation Studied Using Multivariate Copula Downscaling: A Case Study in Northwestern China S. Wang et al. 10.3390/en18081963
61. Spatial Downscaling of Daily Temperature Minima Using Machine Learning Methods and Application to Frost Forecasting in Two Alpine Valleys S. Bhakare et al. 10.3390/atmos16010038
62. An improved deep learning procedure for statistical downscaling of climate data A. Kheir et al. 10.1016/j.heliyon.2023.e18200
63. A Novel Reference-Based and Gradient-Guided Deep Learning Model for Daily Precipitation Downscaling L. Xiang et al. 10.3390/atmos13040511
64. Leveraging Land Cover Priors for Isoprene Emission Super-Resolution C. Ummerle et al. 10.3390/rs17101715
65. pyESDv1.0.1: an open-source Python framework for empirical-statistical downscaling of climate information D. Boateng & S. Mutz 10.5194/gmd-16-6479-2023
66. On the use of convolutional neural networks for downscaling daily temperatures over southern South America in a climate change scenario R. Balmaceda-Huarte et al. 10.1007/s00382-023-06912-6
67. High-resolution GNSS troposphere tomography through explainable deep learning-based downscaling framework S. Haji-Aghajany et al. 10.1186/s43020-025-00177-6
68. A multiscale approach to statistical downscaling of daily precipitation: Israel as a test case D. Sandler et al. 10.1002/joc.8315
69. Spatial Downscaling of Sea Level Anomaly Using a Deep Separable Distillation Network S. Shi et al. 10.3390/rs17142428
70. Probabilistic precipitation downscaling for ungauged mountain sites: a pilot study for the Hindu Kush Himalaya M. Girona-Mata et al. 10.5194/hess-29-3073-2025
71. Estimation of Watershed Hydrochemical Responses to Future Climate Changes Based on CMIP6 Scenarios in the Tianhe River (China) J. Sha et al. 10.3390/su131810102
72. Downscaling of environmental indicators: A review S. Li et al. 10.1016/j.scitotenv.2024.170251
73. A data-driven framework for the stochastic reconstruction of small-scale features with application to climate data sets Z. Wan et al. 10.1016/j.jcp.2021.110484
74. Improving Subseasonal-to-Seasonal forecasts in predicting the occurrence of extreme precipitation events over the contiguous U.S. using machine learning models L. Zhang et al. 10.1016/j.atmosres.2022.106502
75. Statistical Downscaling of SEVIRI Land Surface Temperature to WRF Near-Surface Air Temperature Using a Deep Learning Model A. Afshari et al. 10.3390/rs15184447
76. Machine Learning Methods in Weather and Climate Applications: A Survey L. Chen et al. 10.3390/app132112019
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78. Adjusting spatial dependence of climate model outputs with cycle-consistent adversarial networks B. François et al. 10.1007/s00382-021-05869-8
79. Improving the statistical downscaling performance of climatic parameters with convolutional neural networks A. Hosseini Baghanam et al. 10.2166/wcc.2024.592
80. Convolutional conditional neural processes for local climate downscaling A. Vaughan et al. 10.5194/gmd-15-251-2022
81. A dataset of 0.05-degree leaf area index in China during 1983–2100 based on deep learning network H. Li et al. 10.1038/s41597-024-03948-z
82. A refined method for the simulation of catchment rainfall–runoff based on satellite–precipitation downscaling J. Jian et al. 10.1016/j.jhydrol.2025.132795
83. Mid- to Long-Term Runoff Prediction Based on Deep Learning at Different Time Scales in the Upper Yangtze River Basin Y. Ren et al. 10.3390/w14111692
84. Bias Correction Methods Applied to Satellite Rainfall Products over the Western Part of Saudi Arabia I. Elsebaie et al. 10.3390/atmos16070772
85. Limitation of super-resolution machine learning approach to precipitation downscaling P. Reddy et al. 10.1038/s41598-025-05880-7
86. Downscaling multi-model climate projection ensembles with deep learning (DeepESD): contribution to CORDEX EUR-44 J. Baño-Medina et al. 10.5194/gmd-15-6747-2022
87. A systematic review of predictor screening methods for downscaling of numerical climate models A. Baghanam et al. 10.1016/j.earscirev.2024.104773
88. Learning to Correct Climate Projection Biases B. Pan et al. 10.1029/2021MS002509
89. On the suitability of a convolutional neural network based RCM-emulator for fine spatio-temporal precipitation A. Doury et al. 10.1007/s00382-024-07350-8
90. Deep Learning-Based Downscaling of Temperatures for Monitoring Local Climate Change Using Global Climate Simulation Data F. Gerges et al. 10.1142/S2811032322500011
91. A new conditional generative adversarial neural network approach for statistical downscaling of the ERA5 reanalysis over the Italian Peninsula I. Manco et al. 10.1016/j.envsoft.2025.106427
92. Downscaling Seasonal Precipitation Forecasts over East Africa with Deep Convolutional Neural Networks T. Asfaw & J. Luo 10.1007/s00376-023-3029-2
93. Statistical downscaling of daily temperature and precipitation over China using deep learning neural models: Localization and comparison with other methods L. Sun & Y. Lan 10.1002/joc.6769
94. Downscaling daily wind speed with Bayesian deep learning for climate monitoring F. Gerges et al. 10.1007/s41060-023-00397-6
95. Pattern-based downscaling of snowpack variability in the western United States N. Gauthier et al. 10.1007/s00382-021-06094-z
96. Statistical spatial downscaling of significant wave height in a regional sea from the global ERA5 dataset B. Yuan et al. 10.1016/j.oceaneng.2025.121100
97. To Accurately and Lightly Downscale the Temperature Field by Deep Learning R. Tie et al. 10.1175/JTECH-D-21-0099.1
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99. Deep-Learning Correction Methods for Weather Research and Forecasting (WRF) Model Precipitation Forecasting: A Case Study over Zhengzhou, China J. Zhang et al. 10.3390/atmos15060631
100. MSG-GAN-SD: A Multi-Scale Gradients GAN for Statistical Downscaling of 2-Meter Temperature over the EURO-CORDEX Domain G. Accarino et al. 10.3390/ai2040036
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133. Real-Time Reconstruction of 3d Ocean Temperature Fields from Reanalysis Data and Satellite and Buoy Surface Measurements B. Champenois & T. Sapsis 10.2139/ssrn.4157603
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