87 citations as recorded by crossref.

1. 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
2. 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
3. An improved deep learning procedure for statistical downscaling of climate data A. Kheir et al. 10.1016/j.heliyon.2023.e18200
4. A Novel Reference-Based and Gradient-Guided Deep Learning Model for Daily Precipitation Downscaling L. Xiang et al. 10.3390/atmos13040511
5. Using Machine Learning to Cut the Cost of Dynamical Downscaling S. Hobeichi et al. 10.1029/2022EF003291
6. 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
7. 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
8. A multiscale approach to statistical downscaling of daily precipitation: Israel as a test case D. Sandler et al. 10.1002/joc.8315
9. ResDeepD: A residual super-resolution network for deep downscaling of daily precipitation over India S. Mishra Sharma & A. Mitra 10.1017/eds.2022.23
10. Application of a bivariate bias-correction approach to yield long-term attributes of Indian precipitation and temperature C. Gupta & R. Bhowmik 10.3389/fclim.2023.1067960
11. Fusion of Surface Soil Moisture Data for Spatial Downscaling of Daily Satellite Precipitation Data Q. Wang et al. 10.1109/JSTARS.2023.3336930
12. 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
13. Generating a long-term (2003−2020) hourly 0.25° global PM2.5 dataset via spatiotemporal downscaling of CAMS with deep learning (DeepCAMS) Y. Xiao et al. 10.1016/j.scitotenv.2022.157747
14. Deep generative model super-resolves spatially correlated multiregional climate data N. Oyama et al. 10.1038/s41598-023-32947-0
15. Efficient Super‐Resolution of Near‐Surface Climate Modeling Using the Fourier Neural Operator P. Jiang et al. 10.1029/2023MS003800
16. A comparative study of convolutional neural network models for wind field downscaling K. Höhlein et al. 10.1002/met.1961
17. Downscaling of environmental indicators: A review S. Li et al. 10.1016/j.scitotenv.2024.170251
18. 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
19. 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
20. The impact of global changes in near-term climate forcers on East Africa’s climate R. Opio et al. 10.1186/s40068-023-00304-9
21. 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
22. Machine Learning Methods in Weather and Climate Applications: A Survey L. Chen et al. 10.3390/app132112019
23. Customized deep learning for precipitation bias correction and downscaling F. Wang et al. 10.5194/gmd-16-535-2023
24. Downscaling CORDEX Through Deep Learning to Daily 1 km Multivariate Ensemble in Complex Terrain D. Quesada‐Chacón et al. 10.1029/2023EF003531
25. Adjusting spatial dependence of climate model outputs with cycle-consistent adversarial networks B. François et al. 10.1007/s00382-021-05869-8
26. Statistical downscaling of daily precipitation over southeastern South America: Assessing the performance in extreme events M. Olmo & M. Bettolli 10.1002/joc.7303
27. Predictor Selection for CNN-based Statistical Downscaling of Monthly Precipitation D. Yang et al. 10.1007/s00376-022-2119-x
28. Using Explainability to Inform Statistical Downscaling Based on Deep Learning Beyond Standard Validation Approaches J. González‐Abad et al. 10.1029/2023MS003641
29. Convolutional conditional neural processes for local climate downscaling A. Vaughan et al. 10.5194/gmd-15-251-2022
30. 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
31. 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
32. A systematic review of predictor screening methods for downscaling of numerical climate models A. Baghanam et al. 10.1016/j.earscirev.2024.104773
33. Learning to Correct Climate Projection Biases B. Pan et al. 10.1029/2021MS002509
34. Multi-Level Circulation Pattern Classification Based on the Transfer Learning CNN Network Y. Liu et al. 10.3390/atmos13111861
35. Deep Learning-Based Downscaling of Temperatures for Monitoring Local Climate Change Using Global Climate Simulation Data F. Gerges et al. 10.1142/S2811032322500011
36. GSDNet: A deep learning model for downscaling the significant wave height based on NAFNet X. Wu et al. 10.1016/j.seares.2024.102482
37. Downscaling Seasonal Precipitation Forecasts over East Africa with Deep Convolutional Neural Networks T. Asfaw & J. Luo 10.1007/s00376-023-3029-2
38. 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
39. Downscaling daily wind speed with Bayesian deep learning for climate monitoring F. Gerges et al. 10.1007/s41060-023-00397-6
40. Pattern-based downscaling of snowpack variability in the western United States N. Gauthier et al. 10.1007/s00382-021-06094-z
41. To Accurately and Lightly Downscale the Temperature Field by Deep Learning R. Tie et al. 10.1175/JTECH-D-21-0099.1
42. Repeatable high-resolution statistical downscaling through deep learning D. Quesada-Chacón et al. 10.5194/gmd-15-7353-2022
43. Deep Learning for Daily Precipitation and Temperature Downscaling F. Wang et al. 10.1029/2020WR029308
44. Comparative Assessment of Image Super-Resolution Techniques for Spatial Downscaling of Gridded Rainfall Data S. Golla et al. 10.1007/s42979-024-02653-3
45. 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
46. Multi‐objective downscaling of precipitation time series by genetic programming T. Zerenner et al. 10.1002/joc.7172
47. A critical view on the suitability of machine learning techniques to downscale climate change projections: Illustration for temperature with a toy experiment A. Hernanz et al. 10.1002/asl.1087
48. Deep learning for statistical downscaling of sea states M. Michel et al. 10.5194/ascmo-8-83-2022
49. 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
50. Assessing Three Perfect Prognosis Methods for Statistical Downscaling of Climate Change Precipitation Scenarios M. Legasa et al. 10.1029/2022GL102525
51. Application of a weighted ensemble forecasting method based on online learning in subseasonal forecast in the South China F. Xin et al. 10.1186/s40562-024-00319-9
52. Physics-informed machine learning: case studies for weather and climate modelling K. Kashinath et al. 10.1098/rsta.2020.0093
53. Regional climate model emulator based on deep learning: concept and first evaluation of a novel hybrid downscaling approach A. Doury et al. 10.1007/s00382-022-06343-9
54. Deep learning-based spatial downscaling and its application for tropical cyclone detection in the western North Pacific A. Chen & C. Yuan 10.3389/feart.2024.1345714
55. A Posteriori Random Forests for Stochastic Downscaling of Precipitation by Predicting Probability Distributions M. Legasa et al. 10.1029/2021WR030272
56. Deep Learning for Daily 2‐m Temperature Downscaling S. Ding et al. 10.1029/2023EA003227
57. Downscaling Climatic Variables at a River Basin Scale: Statistical Validation and Ensemble Projection under Climate Change Scenarios R. El-Samra et al. 10.3390/cli12020027
58. spateGAN: Spatio‐Temporal Downscaling of Rainfall Fields Using a cGAN Approach L. Glawion et al. 10.1029/2023EA002906
59. Assessing statistical downscaling in Argentina: Daily maximum and minimum temperatures R. Balmaceda‐Huarte & M. Bettolli 10.1002/joc.7733
60. Using CNN framework to improve multi-GCM ensemble predictions of monthly precipitation at local areas: An application over China and comparison with other methods L. Sun et al. 10.1016/j.jhydrol.2023.129866
61. A container-based workflow for distributed training of deep learning algorithms in HPC clusters J. González-Abad et al. 10.1007/s10586-022-03798-7
62. Downscaling long lead time daily rainfall ensemble forecasts through deep learning H. Jin et al. 10.1007/s00477-023-02444-x
63. High-resolution downscaling of CMIP6 Earth system and global climate models using deep learning for Iberia P. Soares et al. 10.5194/gmd-17-229-2024
64. Exploring super-resolution spatial downscaling of several meteorological variables and potential applications for photovoltaic power A. Damiani et al. 10.1038/s41598-024-57759-8
65. On the modern deep learning approaches for precipitation downscaling B. Kumar et al. 10.1007/s12145-023-00970-4
66. Downscaling atmospheric chemistry simulations with physically consistent deep learning A. Geiss et al. 10.5194/gmd-15-6677-2022
67. Downscaled hyper-resolution (400 m) gridded datasets of daily precipitation and temperature (2008–2019) for the East–Taylor subbasin (western United States) U. Mital et al. 10.5194/essd-14-4949-2022
68. 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
69. An attention mechanism based convolutional network for satellite precipitation downscaling over China Y. Jing et al. 10.1016/j.jhydrol.2022.128388
70. On deep learning-based bias correction and downscaling of multiple climate models simulations F. Wang & D. Tian 10.1007/s00382-022-06277-2
71. Computing the Ensemble Spread From Deterministic Weather Predictions Using Conditional Generative Adversarial Networks R. Brecht & A. Bihlo 10.1029/2022GL101452
72. High-resolution downscaling with interpretable deep learning: Rainfall extremes over New Zealand N. Rampal et al. 10.1016/j.wace.2022.100525
73. Evaluation of statistical downscaling methods for climate change projections over Spain: Present conditions with perfect predictors A. Hernanz et al. 10.1002/joc.7271
74. Convolutional neural network-based statistical post-processing of ensemble precipitation forecasts W. Li et al. 10.1016/j.jhydrol.2021.127301
75. Statistical Downscaling of Temperature Distributions in Southwest China by Using Terrain-Guided Attention Network G. Liu et al. 10.1109/JSTARS.2023.3239109
76. Deep-learning post-processing of short-term station precipitation based on NWP forecasts Q. Liu et al. 10.1016/j.atmosres.2023.107032
77. Generating Fine-Scale Aerosol Data through Downscaling with an Artificial Neural Network Enhanced with Transfer Learning M. Wang et al. 10.3390/atmos13020255
78. A two-stage downscaling hydrological modeling approach via convolutional conditional neural process and geostatistical bias correction Z. Cui et al. 10.1016/j.jhydrol.2023.129498
79. 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
80. 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
81. 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
82. Bias correction for spatially interpolated daily mean air temperature during winter in eastern Hokkaido using multimodal machine learning K. MURAKAMI et al. 10.2480/agrmet.D-20-00027
83. On the suitability of deep convolutional neural networks for continental-wide downscaling of climate change projections J. Baño-Medina et al. 10.1007/s00382-021-05847-0
84. Multi-model ensemble of statistically downscaled GCMs over southeastern South America: historical evaluation and future projections of daily precipitation with focus on extremes O. ME et al. 10.1007/s00382-022-06236-x
85. Statistical downscaling with the downscaleR package (v3.1.0): contribution to the VALUE intercomparison experiment J. Bedia et al. 10.5194/gmd-13-1711-2020
86. Spatio-Temporal Downscaling of Climate Data Using Convolutional and Error-Predicting Neural Networks A. Serifi et al. 10.3389/fclim.2021.656479
87. A Physics-Aware Neural Network Approach for Flow Data Reconstruction From Satellite Observations L. Schweri et al. 10.3389/fclim.2021.656505