13 citations as recorded by crossref.

1. Advancing isotope‐enabled hydrological modelling for ungauged calibration of data‐scarce humid tropical catchments A. Watson et al. 10.1002/hyp.15065
2. Downscaling sea surface height and currents in coastal regions using convolutional neural network B. Yuan et al. 10.1016/j.apor.2024.104153
3. Deep Learning for Bias‐Correcting CMIP6‐Class Earth System Models P. Hess et al. 10.1029/2023EF004002
4. MAUNet: a max-average neural network architecture for precipitation downscaling S. Mishra Sharma & A. Mitra 10.1007/s00521-024-10012-9
5. Deep learning-based bias correction of ISMR simulated by GCM S. Sharma et al. 10.1016/j.atmosres.2024.107589
6. A precipitation downscaling method using a super-resolution deconvolution neural network with step orography P. Reddy et al. 10.1017/eds.2023.18
7. A deep learning-based bias correction model for Arctic sea ice concentration towards MITgcm S. Yuan et al. 10.1016/j.ocemod.2024.102326
8. Machine Learning-Based Bias Correction of Precipitation Measurements at High Altitude H. Li et al. 10.3390/rs15082180
9. Sub-Seasonal Precipitation Bias-Correction in Thailand Using Attention U-Net With Seasonal and Meteorological Effects T. Faijaroenmongkol et al. 10.1109/ACCESS.2023.3337998
10. Deep Learning Improves GFS Wintertime Precipitation Forecast Over Southeastern China D. Sun et al. 10.1029/2023GL104406
11. Toward an improved ensemble of multi-source daily precipitation via joint machine learning classification and regression H. Chen et al. 10.1016/j.atmosres.2024.107385
12. Intercomparison of Deep Learning Architectures for the Prediction of Precipitation Fields With a Focus on Extremes N. Otero & P. Horton 10.1029/2023WR035088
13. A CMIP6-based multi-model downscaling ensemble to underpin climate change services in Australia M. Grose et al. 10.1016/j.cliser.2023.100368