Assessment of NA-CORDEX regional climate models, reanalysis and in situ gridded-observational data sets against the U.S. Climate Reference Network

Climate models' capability of reproducing the present climate at both global and regional scales still needs improvements. The assessment of model performance critically depends on the data sets used as comparators/references. Reanalysis and gridded observational data sets have been frequently used for this purpose. However, none of these can be considered an accurate reference data set because of their associated uncertainties and full representativity. This paper, for the first time, uses in-situ measurements from National Oceanic and Atmospheric Administration U.S. Climate Reference Network (USCRN) spanning the period 2006-2020 to assess daily temperature and precipitation from a suite of dynamically downscaled regional climate models (RCMs; driven by ERA-Interim) involved in NA-CORDEX. The assessment is also extended to the most recent and widely used Earth system reanalyses (ERA5, ERA-Interim, MERRA2 and NARR) and a few in situ-based gridded data sets (Daymet, PRISM, Livneh and CPC). Results show that biases for the different data sets are seasonally and subregionally dependent. On average, reanalysis and in situ-based gridded data sets are warmer (with biases exceeding 0.3°C) than USCRN year-round, while RCMs are colder (warmer) in winter (summer) with biases ranging from -0.5 (0.9)°C for RCMs at 0.44° to -0.2 (1.4)°C for CRCM5-UQAM-11. In situ-based gridded data sets provide the best performance in most of the Contiguous United States (CONUS) regions compared to reanalyses and RCMs, but still have biases in regions such as the Western mountains and the Pacific Northwest. Furthermore, in most US subregions, reanalysis data sets do not outperform reanalysis-driven RCMs. Likewise, for both reanalysis data sets and RCMs, temperature and precipitation biases vary considerably depending on the local orography, with larger temperature biases for coarser model resolutions and precipitation biases for reanalysis.