Articles | Volume 28, issue 22
https://doi.org/10.5194/hess-28-4903-2024
https://doi.org/10.5194/hess-28-4903-2024
Research article
 | 
18 Nov 2024
Research article |  | 18 Nov 2024

Downscaling precipitation over High-mountain Asia using multi-fidelity Gaussian processes: improved estimates from ERA5

Kenza Tazi, Andrew Orr, Javier Hernandez-González, Scott Hosking, and Richard E. Turner

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Latest update: 25 Dec 2024
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Short summary
This work aims to improve the understanding of precipitation patterns in High-mountain Asia, a crucial water source for around 1.9 billion people. Through a novel machine learning method, we generate high-resolution precipitation predictions, including the likelihoods of floods and droughts. Compared to state-of-the-art methods, our method is simpler to implement and more suitable for small datasets. The method also shows accuracy comparable to or better than existing benchmark datasets.