12 citations as recorded by crossref.

1. A Generative Deep Learning Approach to Stochastic Downscaling of Precipitation Forecasts L. Harris et al. 10.1029/2022MS003120
2. Downscaling Seasonal Precipitation Forecasts over East Africa with Deep Convolutional Neural Networks T. Asfaw & J. Luo 10.1007/s00376-023-3029-2
3. Assessing statistical downscaling in Argentina: Daily maximum and minimum temperatures R. Balmaceda‐Huarte & M. Bettolli 10.1002/joc.7733
4. Characterization of Subsurface Hydrogeological Structures With Convolutional Conditional Neural Processes on Limited Training Data Z. Cui et al. 10.1029/2022WR033161
5. spateGAN: Spatio‐Temporal Downscaling of Rainfall Fields Using a cGAN Approach L. Glawion et al. 10.1029/2023EA002906
6. Downscaling precipitation over High-mountain Asia using multi-fidelity Gaussian processes: improved estimates from ERA5 K. Tazi et al. 10.5194/hess-28-4903-2024
7. ResDeepD: A residual super-resolution network for deep downscaling of daily precipitation over India S. Mishra Sharma & A. Mitra 10.1017/eds.2022.23
8. 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
9. Using Explainability to Inform Statistical Downscaling Based on Deep Learning Beyond Standard Validation Approaches J. González‐Abad et al. 10.1029/2023MS003641
10. 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
11. 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
12. Environmental sensor placement with convolutional Gaussian neural processes T. Andersson et al. 10.1017/eds.2023.22