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.

Model code and software

Deep Downscaling Santander Met Group https://github.com/SantanderMetGroup/DeepDownscaling

downscaleR.keras Santander Met Group https://github.com/SantanderMetGroup/downscaleR.keras

climate4R.value Santander Met Group https://github.com/SantanderMetGroup/climate4R.value

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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.