Articles | Volume 13, issue 4
Geosci. Model Dev., 13, 2109–2124, 2020
https://doi.org/10.5194/gmd-13-2109-2020
Geosci. Model Dev., 13, 2109–2124, 2020
https://doi.org/10.5194/gmd-13-2109-2020

Model experiment description paper 28 Apr 2020

Model experiment description paper | 28 Apr 2020

Configuration and intercomparison of deep learning neural models for statistical downscaling

Jorge Baño-Medina et al.

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AR: Author's response | RR: Referee report | ED: Editor decision
AR by Anna Wenzel on behalf of the Authors (04 Mar 2020)  Author's response
ED: Publish as is (19 Mar 2020) by David Topping
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Short summary
In this study we intercompare different deep learning topologies for statistical downscaling purposes. As compared to the top-ranked methods in the largest-to-date downscaling intercomparison study, our results better predict the local climate variability. Moreover, deep learning approaches can be suitably applied to large regions (e.g., continents), which can therefore foster the use of statistical downscaling in flagship initiatives such as CORDEX.