Evaluation of Historical CMIP6 Model Simulations of Extreme Precipitation Over Contiguous US Regions
The study evaluates simulated historical precipitation in Coupled Model Intercomparison Project Phase 6 (CMIP6) models using precipitation indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI) over the continental US (CONUS) regions. The ETCCDI indices are a standardized set of metrics that sample weather and climate extremes.
The emphasis of this work is on the evaluation of the regional performance of individual models, which is of direct relevance to both stakeholders and scientists. The regional evaluation of models is important for reliable climate model projections.
The study evaluates ETCCDI precipitation indices in CMIP6 models against corresponding indices from the CPC unified gauge-based analyses of precipitation over the CONUS regions. To estimate observational uncertainty, precipitation indices for three other observational datasets (HadEx2, Livneh, and PRISM) are evaluated against the CPC analyses. Both the moderate and extreme mean precipitation intensities are overestimated over the western CONUS and underestimated in the areas of the Central Great Plains (CGP) in most CMIP6 models tested. Biases in the interannual variability of most of the indices have similar patterns to those in corresponding mean biases. Multimodel medians of CMIP6 (CMIP6-MMM) and CMIP5 (CMIP5-MMM) have similar biases in climatology and variability but biases tend to be smaller in CMIP6-MMM. CMIP6-MMM performs better than individual models and even observational datasets in some cases. Differences between observational datasets for most indices are comparable to the CMIP6 interquartile model spread. The better-performing observational and model datasets are different in different parts of the CONUS.