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Three different reference data sets are considered: the coarse\u2010resolved E\u2010OBS data set, a compilation of regional high\u2010resolution gridded products (HR) and the European\u2010scale MESAN reanalysis. Five high\u2010resolution ERA\u2010Interim\u2010driven RCM experiments of the EURO\u2010CORDEX initiative are evaluated against each of these references over eight European sub\u2010regions and considering a range of performance metrics for mean daily temperature and daily precipitation. The spatial scale of the evaluation is 0.22\u00b0, that is, the grid spacing of the coarsest data set in the exercise (E\u2010OBS). While the three reference grids agree on the overall mean climatology, differences can be pronounced over individual regions. These differences partly translate into RCM evaluation uncertainty. For most cases observational uncertainty is smaller than RCM uncertainty. Nevertheless, for individual sub\u2010regions and performance metrics observational uncertainty can dominate. This is especially true for precipitation and for metrics targeting the wet\u2010day frequency, the pattern correlation and the distributional similarity. In some cases the spatially averaged mean bias can also be considerably affected. An illustrative ranking exercise highlights the overall effect of observational uncertainty on RCM ranking. Over individual sub\u2010domains, the choice of a specific reference can modify RCM ranks by up to four levels (out of five RCMs). For most cases, however, RCM ranks are stable irrespective of the reference. These results provide a twofold picture: model uncertainty dominates for most regions and for most performance metrics considered, and observational uncertainty plays a minor role. For individual cases, however, observational uncertainty can be pronounced and needs to be definitely taken into account. 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