Future Increases in North American Extreme Precipitation in CMIP6 Downscaled with LOCA

A new set of CMIP6 data downscaled using the localized constructed analogs (LOCA) statistical method has been produced, covering central Mexico through southern Canada at 6-km resolution. Output from 27 CMIP6 Earth system models is included, with up to 10 ensemble members per model and 3 SSPs (245, 370, and 585). Improvements from the previous CMIP5 downscaled data result in higher daily precipitation extremes, which have significant societal and eco-nomic implications. The improvements are accomplished by using a precipitation training dataset that better represents daily extremes and by implementing an ensemble bias correction that allows a more realistic representation of extreme high daily precipitation values in models with numerous ensemble members. Over southern Canada and the CONUS ex-clusive of Arizona (AZ) and New Mexico (NM), seasonal increases in daily precipitation extremes are largest in winter (-25% in SSP370). Over Mexico, AZ, and NM, seasonal increases are largest in autumn (-15%). Summer is the outlier season, with low model agreement except in New England and little changes in 5-yr return values, but substantial increases in the CONUS and Canada in the 500-yr return value. One-in-100-yr historical daily precipitation events become substan-tially more frequent in the future, as often as once in 30-40 years in the southeastern United States and Pacific Northwest by the end of the century under SSP 370. Impacts of the higher precipitation extremes in the LOCA version 2 downscaled CMIP6 product relative to the LOCA downscaled CMIP5 product, even for similar anthropogenic emissions, may need to be considered by end-users.

Automated summary

A new set of CMIP6 data downscaled using the LOCA statistical method has been produced, which covers central Mexico through southern Canada at a resolution of 6 km. The improvements in daily precipitation extremes compared to CMIP5 downscaled data have important societal and economic implications. These improvements are achieved by using a better precipitation training dataset and implementing an ensemble bias correction.