An extreme-preserving long-term gridded daily precipitation data set for the conterminous United States

Abstract Extreme daily precipitation contributes to flooding that can cause significant economic damages, and so is important properly capture in gridded meteorological data sets. This work examines extremes, the mean on wet days, fraction of days two widely used sets over conterminous U.S. (CONUS). Compared underlying station observations, show a 27% reduction annual 1-day maximum precipitation, 25% increase day fraction, 1.5 2.5 spell length, 30% low bias 20-year return values decrease days. It shown these changes arise primarily from time-adjustment applied put gauge observations into uniform time frame, with gridding process playing lesser role. A new set developed omits (as well as extending by 7 years) perform significantly better reproducing extreme metrics. When force land surface model, annually averaged runoff increases 38% compared original data, 17%, evapotranspiration drops 2.3%, fewer leads 3.3% estimated solar insolation. These are large enough affect portrayals flood risk water balance components for ecological climate-change applications across CONUS.