Traditional approaches for comparing global climate models and observational
data products typically fail to account for the geographic location of the
underlying weather station data. For modern high-resolution models, this is an
oversight since there are likely grid cells where the physical output of a
climate model is compared with a statistically interpolated quantity instead of
actual measurements of the climate system. In this paper, we quantify the
impact of geographic sampling on the relative performance of high resolution
climate models' representation of precipitation extremes in Boreal winter (DJF)
over the contiguous United States (CONUS), comparing model output from five
early submissions to the HighResMIP subproject of the CMIP6 experiment. We find
that properly accounting for the geographic sampling of weather stations can
significantly change the assessment of model performance. Across the models
considered, failing to account for sampling impacts the different metrics
(extreme bias, spatial pattern correlation, and spatial variability) in
different ways (both increasing and decreasing). We argue that the geographic
sampling of weather stations should be accounted for in order to yield a more
straightforward and appropriate comparison between models and observational
data sets, particularly for high resolution models. While we focus on the CONUS
in this paper, our results have important implications for other global land
regions where the sampling problem is more severe