Variability in Precipitation Downscaled from CMIP Atmospheric Rivers over the Pacific Northwest

Evaluating the effect of extreme precipitation in a future climate over mountains of the western US is challenged by the spatial resolution of global climate models. Because these models have only very smooth mountains, they do not explicitly represent orographic precipitation, which is the dominant form of precipitation in the western US. As a result, even simpler statistically downscaled methods may fail to adequately represent relevant changes in precipitation in this domain. Here we use a simplified dynamical method, the Intermediate Complexity Atmospheric Research model (ICAR) to explicitly downscale orographic precipitation from 5 CMIP6 models and 8 CMIP5 climate models to investigate changes in extreme orographic precipitation over the western US. We highlight regions for which the results differ from those of more traditional statistical downscaling techniques, and discuss possible reasons for those differences, e.g. the explicit representation of snow vs rain microphysical processes in a future climate. We also note that based on downscaled results for a single model (CESM2) large ensemble, Northern California and Southern Oregon appear most at risk of being impacted by extreme precipitation events larger than have been observed even in the absence of climate change.