| Item | Content |
|---|---|
| Title | A scalable deep learning framework for daily precipitation downscaling architecture accuracy and adaptability |
| Author | Xiaolong Liu and Meixiu Yu and Fei Feng and Aftab Nazeer and Dewei Wang |
| Journal | Journal of Water and Climate Change |
| Year | 2025 |
| Volume | / |
| Issue | / |
| Pages | jwc2025293 |
| Abstract | Global climate models provide essential large-scale climate projections, yet their coarse spatial resolution (0.5°–4°) limits understanding of urbanization’s impact on regional climate dynamics. This study presents the Deep Residual Network for Precipitation Downscaling (DRN-PD), a modular neural architecture designed to enhance spatial precision and modeling interpretability in daily precipitation downscaling. By integrating CMIP6 atmospheric predictors, high-resolution land use data, and CHIRPS observations, DRN-PD advances understanding of climate–land surface interactions. Applied to the Yangtze Delta Megalopolis, the model achieves 18-fold spatial refinement, generating precipitation outputs at 5 km resolution. It consistently reproduces seasonal precipitation patterns, with mean absolute errors of 2.81–7.28 mm and root mean square errors below 11.28 mm. Spatial … |
| Cited by | / |
| Google Citations view | https://scholar.google.com/citations?view%5Fop=view%5Fcitation&hl=en&citation%5Ffor%5Fview=ly9d4IgAAAAJ:ldfaerwXgEUC |
| Url | https://iwaponline.com/jwcc/article/doi/10.2166/wcc.2025.293/110681 |
| Achive | </> |
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