46 citations as recorded by crossref.

1. A systematic review of the NASA Land Information System (LIS): Two decades of advancements in hydrological modeling, data assimilation, and operational earth system applications S. Marshall et al. https://doi.org/10.1016/j.jhydrol.2025.134784
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3. Improved Near-Real-Time Precipitation Estimation From Himawari-8 Data and Gauge Observations in the Xiangjiang River Basin Using a Three-Stage Machine Learning Framework S. Yan et al. https://doi.org/10.1109/JSTARS.2025.3633323
4. Sub-Seasonal Precipitation Bias-Correction in Thailand Using Attention U-Net With Seasonal and Meteorological Effects T. Faijaroenmongkol et al. https://doi.org/10.1109/ACCESS.2023.3337998
5. Deep Learning Improves GFS Wintertime Precipitation Forecast Over Southeastern China D. Sun et al. https://doi.org/10.1029/2023GL104406
6. Enhancing IMERG precipitation estimates using machine learning and bias correction across elevation zones in India L. Nongmaithem et al. https://doi.org/10.1080/02626667.2025.2549422
7. Toward an improved ensemble of multi-source daily precipitation via joint machine learning classification and regression H. Chen et al. https://doi.org/10.1016/j.atmosres.2024.107385
8. SPATIAL DOWNSCALING OF DAILY PRECIPITATION d4PDF DATA USING ANN ALGORITHM I. KHATEEB et al. https://doi.org/10.2208/journalofjsce.24-00156
9. Evaluating Bias Correction Methods Using Annual Maximum Series Rainfall Data from Observed and Remotely Sensed Sources in Gauged and Ungauged Catchments in Uganda M. Okirya & J. Du Plessis https://doi.org/10.3390/hydrology12050113
10. A scalable deep learning framework for daily precipitation downscaling: architecture, accuracy, and adaptability X. Liu et al. https://doi.org/10.2166/wcc.2025.293
11. Multivariate bias correction and downscaling of climate models with trend-preserving deep learning F. Wang & D. Tian https://doi.org/10.1007/s00382-024-07406-9
12. Impact of deep learning-driven precipitation corrected data using near real-time satellite-based observations and model forecast in an integrated hydrological model K. Patakchi Yousefi et al. https://doi.org/10.3389/frwa.2024.1439906
13. 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
14. Evaluating Geostatistical and Statistical Merging Methods for Radar–Gauge Rainfall Integration: A Multi-Method Comparative Study X. Le et al. https://doi.org/10.3390/rs17152622
15. Assessing the effectiveness of ANN model in spatial downscaling of d4PDF hourly precipitation data: a case study in Japan I. Khateeb et al. https://doi.org/10.1007/s44367-025-00003-5
16. Machine Learning-Based Bias Correction of Precipitation Measurements at High Altitude H. Li et al. https://doi.org/10.3390/rs15082180
17. Kernel-based dynamic ensemble approach for classifying imbalanced data with overlapping classes S. Abokadr et al. https://doi.org/10.1038/s41598-026-42940-y
18. Testing machine learning algorithms as post-processing tools for hydro-meteorological modelling over a small river basin C. Xu et al. https://doi.org/10.1016/j.envsoft.2025.106592
19. Limitation of super-resolution machine learning approach to precipitation downscaling P. Reddy et al. https://doi.org/10.1038/s41598-025-05880-7
20. Intercomparison of Deep Learning Architectures for the Prediction of Precipitation Fields With a Focus on Extremes N. Otero & P. Horton https://doi.org/10.1029/2023WR035088
21. Advancing isotope‐enabled hydrological modelling for ungauged calibration of data‐scarce humid tropical catchments A. Watson et al. https://doi.org/10.1002/hyp.15065
22. Improved modelling of mountain snowpacks with spatially distributed precipitation bias correction derived from historical reanalysis M. von Kaenel & S. Margulis https://doi.org/10.5194/tc-19-3309-2025
23. Explainable sequence-aware deep learning based decadal shoreline modelling in the Southern Baltic until 2050 K. Tanwari et al. https://doi.org/10.1016/j.envsoft.2026.106954
24. Successful Precipitation Downscaling Through an Innovative Transformer-Based Model F. Yang et al. https://doi.org/10.3390/rs16224292
25. Accurate and efficient AI-assisted paradigm for adding granularity to ERA5 precipitation reanalysis M. Cavaiola et al. https://doi.org/10.1038/s41598-024-77542-z
26. Deep learning based bias correction of TRMM precipitation estimates using IMD-gridded precipitation as ground observation S. Mishra Sharma & A. Mitra https://doi.org/10.1017/eds.2024.39
27. Deep Learning-Based Multi-Source Precipitation Fusion and Its Utility for Hydrological Simulation Z. Huang et al. https://doi.org/10.3390/atmos17010070
28. A precipitation downscaling method using a super-resolution deconvolution neural network with step orography P. Reddy et al. https://doi.org/10.1017/eds.2023.18
29. Spatiotemporal Super-Resolution of Satellite Sea Surface Salinity Based on a Progressive Transfer Learning-Enhanced Transformer Z. Liang et al. https://doi.org/10.3390/rs17152735
30. 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
31. Robust deep learning-based downscaling of mean and extreme precipitation over the Indian subcontinent M. Murukesh & P. Kumar https://doi.org/10.1088/2752-5295/ae6885
32. Fusing Multi-Scale Numerical Model Forecasts to Improve Short-Term Intense Rainfall Forecast with a Deep Learning Rain Network Q. Zhong et al. https://doi.org/10.1007/s13351-025-4151-0
33. Performance of Kilometer-Scale CARAS Precipitation Product Against Ground-based Observations During 2008–2021 over Hubei, China C. ZHU et al. https://doi.org/10.3724/j.1006-8775.2024.036
34. Projecting future climate extremes in the glacier-fed upper indus basin using machine learning based downscaling of CMIP6 GCMs M. Saleem et al. https://doi.org/10.1007/s00704-025-05793-5
35. Current progress in subseasonal-to-decadal prediction based on machine learning Z. Shen et al. https://doi.org/10.1016/j.acags.2024.100201
36. Statistical spatial downscaling of significant wave height in a regional sea from the global ERA5 dataset B. Yuan et al. https://doi.org/10.1016/j.oceaneng.2025.121100
37. Downscaling sea surface height and currents in coastal regions using convolutional neural network B. Yuan et al. https://doi.org/10.1016/j.apor.2024.104153
38. Deep Learning for Bias‐Correcting CMIP6‐Class Earth System Models P. Hess et al. https://doi.org/10.1029/2023EF004002
39. A visual analytics approach to exploring regional physical processes reflected in generative climate downscaling models P. Chang et al. https://doi.org/10.1177/14738716261434910
40. Deep learning-based bias correction of ISMR simulated by GCM S. Sharma et al. https://doi.org/10.1016/j.atmosres.2024.107589
41. A tailored deep learning method to improve spatial rainfall downscaling T. Li et al. https://doi.org/10.1016/j.jhydrol.2026.135272
42. A deep learning-based bias correction model for Arctic sea ice concentration towards MITgcm S. Yuan et al. https://doi.org/10.1016/j.ocemod.2024.102326
43. Enhancing precipitation intensity estimation using ERA5-land reanalysis with statistical and machine learning approaches A. Abdolmanafi et al. https://doi.org/10.1016/j.rineng.2025.104928
44. A Data-Driven Approach to Improve Cocoa Crop Establishment in Colombia: Insights and Agricultural Practice Recommendations from an Ensemble Machine Learning Model L. Talero-Sarmiento et al. https://doi.org/10.3390/agriengineering7010006
45. START: A Hybrid Spatio-Temporal Attention ResNet Transformer for Explainable Multivariable Meteorological Bias-correction D. Singh et al. https://doi.org/10.1007/s41748-026-01132-4
46. Assessing the impact of AI-based meteorological postprocessing on seasonal hydrological forecasting skill in Mediterranean semi-arid basins D. De León Pérez et al. https://doi.org/10.1016/j.ejrh.2026.103535