Articles | Volume 16, issue 13
https://doi.org/10.5194/gmd-16-3785-2023
https://doi.org/10.5194/gmd-16-3785-2023
Model description paper
 | 
11 Jul 2023
Model description paper |  | 11 Jul 2023

Deep learning for stochastic precipitation generation – deep SPG v1.0

Leroy J. Bird, Matthew G. W. Walker, Greg E. Bodeker, Isaac H. Campbell, Guangzhong Liu, Swapna Josmi Sam, Jared Lewis, and Suzanne M. Rosier

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Short summary
Deriving the statistics of expected future changes in extreme precipitation is challenging due to these events being rare. Regional climate models (RCMs) are computationally prohibitive for generating ensembles capable of capturing large numbers of extreme precipitation events with statistical robustness. Stochastic precipitation generators (SPGs) provide an alternative to RCMs. We describe a novel single-site SPG that learns the statistics of precipitation using a machine-learning approach.