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. 2020 Jul 23;7:248. doi: 10.1038/s41597-020-00587-y

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© The Author(s) 2020

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 1 — Taylor plots for comparisons between the original GCM data (blue) and CHELSA_CMIP5ts data (red) for all four GCMs (ACCESS1-3, CESM1-BGC, CMCC-CM, MIROC5). The original data does not perform well with respect to precipitation (ps), but this performance is improved by the downscaling algorithm, although the standard deviation of the downscaled data is slightly higher than that of the original GCMs. For both maximum (tasmax), and minimum (tasmin) temperatures, the original GCM data already shows a good performance, which is further increased by the downscaling to higher resolutions.