The intensification of extreme precipitation in a warming climate is expected to increase flood risk. In order to support flood resilience efforts, it is important to anticipate and quantify potential changes in design standards under future climate conditions. This study assessed how extreme precipitation is expected to change over the 21st century in relation to current National Oceanic and Atmospheric Administration (NOAA) Atlas 14 design standards over the contiguous United States (CONUS). We used the Community Earth System Model Version 2 large ensemble (CESM2‐LE) simulations from the Coupled Model Intercomparison Project Phase 6 and incorporated future changes into flood engineering design standard with a spatially distributed quantile delta mapping method. Relative changes in extreme daily precipitation were computed for multiple average recurrence intervals (ARIs) up to 100‐year and different planning horizons (2020, 2040, 2060, 2080, and 2100). The results indicated an intensification of extreme precipitation by approximately 10%–40% in northern regions and 20%–80% in southern regions by 2100. The current 100‐year ARI with 24‐hr duration from NOAA Atlas 14 is projected to become the 50‐year ARI in the Northern Great Plains, less than the 25‐year ARI in Southwest areas, and approximately the 25‐year ARI in the other regions by 2100. While a nationwide consensus is still needed, this work presents a possible methodology for incorporating climate uncertainty in engineering design. A comparison across major metropolitan areas also illustrates regional variability in projected changes relative to NOAA Atlas 14, suggesting a need for varied local‐scale responses.

Precipitation estimates are highly uncertain in complex regions such as High Mountain Asia (HMA), where ground measurements are very difficult to obtain and atmospheric dynamics poorly understood. Though gridded products derived from satellite-based observations and/or reanalysis can provide temporally and spatially distributed estimates of precipitation, there are significant inconsistencies in these products. As such, to date, there is little agreement in the community on the best and most accurate gridded precipitation product in HMA, which is likely area dependent because of HMA’s strong heterogeneities and complex orography. Targeting these gaps, this article presents the development of a consensus ensemble precipitation product using three gridded precipitation datasets [the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), the Climate Hazards Group Infrared Precipitation with Station data (CHIRPS), and the ECMWF reanalysis ERA5] with a localized probability matched mean (LPM) approach. We evaluate the performance of the LPM estimate along with a simple ensemble mean (EM) estimate to overcome the differences and disparities of the three selected constituent products on long-term averages and trends in HMA. Our analysis demonstrates that LPM reduces the high biases embedded in the ensemble members and provides more realistic spatial patterns compared to EM. LPM is also a good alternative for merging data products with different spatiotemporal resolutions. By filtering disparities among the individual ensemble members, LPM overcomes the problem of a certain product performing well only in a particular area and provides a consensus estimate with plausible temporal trends.

The investigation of regional vulnerability to extreme hydroclimatic events (e.g., floods and hurricanes) is quite challenging due to its dependence on reliable precipitation estimates. Better understanding of past precipitation trends is crucial to examine changing precipitation extremes, optimize future water demands, stormwater infrastructure, extreme event measures, irrigation management, etc., especially if combined with future climate and population projections. The objective of the study is to investigate the spatial-temporal variability of average and extreme precipitation at a sub-regional scale, specifically in the Southern Mid-Atlantic United States, a region characterized by diverse topography and is among the fastest-growing areas in North America. Particularly, this work investigates past precipitation trends and patterns using the North American Land Data Assimilation System, Version 2 (NLDAS-2, 12 km/1 h resolution) reanalysis dataset during 1980–2018. Both parametric (linear regression) and non-parametric (e.g., Theil-Sen) robust statistical tools are employed in the study to analyze trend magnitudes, which are tested for statistical significance using the Mann-Kendall test. Standard precipitation indices from ETCCDI are also used to characterize trends in the relative contribution of extreme events to precipitation in the area. In the region an increasing trend (4.3 mm/year) is identified in annual average precipitation with ~34% of the domain showing a significant increase (at the 0.1 significance level) of +3 to +5 mm/year. Seasonal and sub-regional trends are also investigated, with the most pronounced increasing trends identified during summers along the Virginia and Maryland border. The study also finds a statistically significant positive trend (at a 0.05 significance level) in the annual maximum precipitation. Furthermore, the number of daily extremes (daily total precipitation higher than the 95th and 99th percentiles) also depicts statistically significant increases, indicating the increased frequency of extreme precipitation events. Investigations into the proportion of annual precipitation occurring on wet days and extremely wet days (95th and 99th percentile) also indicate a significant increase in their relative contribution. The findings of this study have the potential to improve local-scale decision-making in terms of river basin management, flood control, irrigation scheme scheduling, and stormwater infrastructure planning to address urban resilience to hydrometeorological hazards.

Global climate models and long-term observational records point to the intensification of extreme precipitation due to global warming. Such intensification has direct implications for worsening floods and damage to life and property. This study investigates the projected trends (2015–2100) in precipitation climatology and daily extremes using Community Earth System Model Version 2 large ensemble (CESM2-LE) simulations at regional and seasonal scales. Specifically, future extreme precipitation is examined in National Climate Assessment (NCA) regions over the Contiguous United States using SSP3-7.0 (Shared Socioeconomic Pathway). Extreme precipitation is analyzed in terms of daily maximum precipitation and simple daily intensity index (SDII) using Mann-Kendall (5% significance level) and Theil-Sen (TS) regression. The most substantial increases occur in the highest precipitation values (95th) during summer and winter clustered in the Midwest and Northeast, respectively, according to long-term extreme trends evaluated in quantiles (i.e., 25, 50, 75, and 95th). Seasonal climatology projections suggest wetting and drying patterns, with wetting in spring and winter in the eastern areas and drying during summer in the Midwest. Lower quantiles in the central U.S. are expected to remain unchanged, transitioning to wetting patterns in the fall due to heavier precipitation. Winter positive trends (at a 5% significance level) are most prevalent in the Northeast and Southeast, with an overall ensemble agreement on such trends. In spring, these trends are predominantly found in the Midwest. In the Northeast and Northern Great Plains, the intensity index shows a consistent wetting pattern in spring, winter, and summer, whereas a drying pattern is projected in the Midwest during summer. Normalized regional changes are a function of indices, quantiles, and seasons. Specifically, seasonal accumulations present larger changes (~30% and above) in summer and lower changes (< ~20%) in winter in the Southern Great Plains and the Southwestern U.S. Examining projections of extreme precipitation change across distinct quantiles provides insights into the projected variability of regional precipitation regimes over the coming decades.

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