New gridded large multi-model ensembles of empirical-statistical downscaling results

Downscaled multi-model ensemble mean and standard deviation through empirical-statistical downscaling have been gridded to a 5 arc-minute spatial resolution (~5km) for key seasonal temperature and precipitation statistics for the Barents Sea region, the Nordic countries, Europe, and other parts of the world. The downscaling has made use of a common EOF basis for ensuring a good representation of the predictors in terms of their spatio-temporal covariance structure, and for facilitating a quality control of the predictors. The downscaled key statistical parameters provide information about daily variables, such as the number of days with heavy precipitation. One objective has been to derive robust and reliable data, and ensemble sizes of hundreds of runs based on multiple global climate models provide a basis for robust estimates, since the addition of exclusion of a few runs don’t change the results significantly. The  downscaled results also provide reliable information on temperature and precipitation, and the reliability of the results is tested against ensemble spread properties as well as the consistency between the ensemble members and the observed evolution for a common historical period. Three diagnostics are included in the netCDF files which indicate how close the ensemble spread is to being normal, how similar the historical trends are, and how the observed interannual variability compares with the ensemble range. The data include CMIP6 SSPs 1-26, 2-45, 3-70, 5-85 as well as CMIP5 RCPs 45, 26 and 85. Details about the predictors and predictands are provided in the netCDFfile header. The data can be openly accessed through OpenDap/thredds as well as FigShare, and it provides a basis for regional climate information for society (RIfS) as well as climate change impact studies. This data represents a new contribution from the CORDEX community.