Observed and Projected Monthly Precipitation Distribution Shifts as Hydroclimatic Indicators for Regional Water-Resource Assessment Using PRISM and NEX-GDDP-CMIP6

Monthly precipitation distributions provide screening-level hydroclimatic information relevant to regional water-resource assessment that is not captured by annual or seasonal means alone. This study evaluates observed and projected shifts in monthly precipitation distributions across the nine NOAA Climate Regions of the contiguous United States using PRISM observations and NEX-GDDP-CMIP6 projections. Observed changes were assessed using PRISM monthly precipitation for 1941–2020 by comparing 1941–1980 with 1981–2020, with sensitivity tests using 1981–2014 and an alternative bootstrap block length. Future changes were evaluated using five NEX-GDDP-CMIP6 models under four SSP scenarios, relative to each model’s 1981–2014 historical reference. To strengthen the statistical and water-resource interpretation, the analysis additionally evaluates historical CMIP6 performance against PRISM, tests explicit hypotheses using permutation/bootstrap procedures with Benjamini–Hochberg false-discovery-rate control, and introduces a categorical Hydroclimatic Screening Matrix. The selected models reproduced the regional monthly climatological cycle well, with monthly climatology correlations of 0.943–0.969 and monthly climatology Kling–Gupta Efficiency values of 0.834–0.886, although raw monthly time-series skill was lower. Observed PRISM results show robust mean increases in the Northeast, Upper Midwest, and Ohio Valley, while monthly Q95 increased robustly in the Northeast and South. Future projections indicate broad late-century increases in monthly mean and upper-tail precipitation, especially under SSP5-8.5. Statistical testing showed that late-century SSP5-8.5 projected changes fell outside the empirical PRISM historical-change envelope in 14 of 18 region-metric pairs, whereas upper-tail amplification was not FDR-significant across regions. The Hydroclimatic Screening Matrix identifies aligned planning-relevant signals, emerging upper-tail concerns, seasonal-storage follow-up priorities, and directional-uncertainty cases. These results support precipitation-based screening for identifying where detailed hydrologic impact modeling may be warranted, without treating monthly precipitation metrics as direct runoff, recharge, reservoir-operation, or flood-risk estimates. The observational endpoint of 2020 was selected to form two equal 40-year periods, thereby keeping the sample lengths balanced for distributional comparisons.