Comparison of machine learning statistical downscaling and regional climate models for temperature, precipitation, wind speed, humidity and radiation over Europe under present conditions

There are two main approaches to downscale global climate projections: dynamical and statistical downscaling. Both families have been widely evaluated, but intercomparison studies between the two families are scarce, and usually limited to temperature and precipitation. In this work, we present a comparison between a statistical downscaling model (SDM) based on machine learning and six regional climate models (RCMs) from EURO-CORDEX, for five variables of interest: temperature, precipitation, wind, humidity and solar radiation under present climatic conditions. The study is conducted at a continental scale over Europe, with a spatial resolution of 0.11 & DEG; and daily data. Both the SDM and the RCMs are driven by the ERA-Interim reanalysis, and observations are taken from the gridded dataset E-OBS. Several aspects have been evaluated: daily series, mean values and extremes, spatial patterns and also temporal aspects. Additionally, a multivariable index (fire weather index) derived from the fundamental variables has been included. The SDM has better scores than the RCMs for all the evaluated metrics with only a few exceptions, mainly related to an underestimation of the variance. After bias correction, both the SDM and the six RCMs present similar results, with no significant differences among them. Results presented here, combined with the low computational expense of SDMs and the limited availability of RCMs over some CORDEX domains, should motivate the consideration of statistical downscaling at the same level as RCMs by official providers of regional information, and its inclusion in reference sites. Nonetheless, further analysis on crucial aspects such as the impact on long-term trends or the sensitivity of different methods to being driven by global climate models, is needed.