Articles | Volume 19, issue 16
https://doi.org/10.5194/gmd-19-7545-2026
© Author(s) 2026. This work is distributed under the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
17 Aug 2026
Model evaluation paper |
| 17 Aug 2026
| 17 Aug 2026
Comprehensive inter-comparison of generative AI models for super-resolution precipitation downscaling across hydroclimatic regimes
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Hydrol. Earth Syst. Sci., 29, 4929–4949, 2025
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Cited articles
Abdelmoaty, H. M., Papalexiou, S. M., Mamalakis, A., Singh, S., Coia, V., Hairabedian, M., Szeftel, P., and Grover, P.: Generative Adversarial Networks for Downscaling Hourly Precipitation in the Canadian Prairies, Journal of Geophysical Research: Machine Learning and Computation, 2, https://doi.org/10.1029/2025JH000678, 2025.
Abdelmoaty, H. M., Papalexiou, S. M., Mamalakis, A., Singh, S., Coia, V., Hairabedian, M., Szeftel, P., and Grover, P.: Does Non-Stationarity Affect GAN-Based Downscaling? Insights from High-Resolution WRF Simulations over the Canadian Prairies, Artificial Intelligence for the Earth Systems, (Under Review), 2026.
Arjovsky, M., Chintala, S., and Bottou, L.: Wasserstein GAN, 2017.
Baño-Medina, J., Manzanas, R., and Gutiérrez, J. M.: Configuration and intercomparison of deep learning neural models for statistical downscaling, Geosci. Model Dev., 13, 2109–2124, https://doi.org/10.5194/gmd-13-2109-2020, 2020.
Bednarz, T. and Cherukuri, R.: Use of Physics-Based AI for Simulations and Modeling in the Era of Digital Twins, in: SIGGRAPH Asia 2023 Courses, 1–45, https://doi.org/10.1145/3610538.3614627, 2023.
