Flash floods are largely driven by high rainfall rates in convective storms that are projected to increase in frequency and intensity in a warmer climate in the future. However, quantifying the changes in future flood flashiness is challenging due to the lack of high-resolution climate simulations. Here we use outputs from a continental convective-permitting numerical weather model at 4-km and hourly resolution and force a numerical hydrologic model at a continental scale to depict such change. As results indicate, US floods are becoming 7.9% flashier by the end of the century assuming a high-emissions scenario. The Southwest (+10.5%) has the greatest increase in flashiness among historical flash flood hot spots, and the central US (+8.6%) is emerging as a new flash flood hot spot. Additionally, future flash flood-prone frontiers are advancing northwards. This study calls on implementing climate-resilient mitigation measures for emerging flash flood hot spots.

Quantifying uncertainties of precipitation estimation, especially in extreme events, could benefit early warning of water-related hazards like flash floods and landslides. Rain gauges, weather radars, and satellites are three mainstream data sources used in measuring precipitation but have their own inherent advantages and deficiencies. With a focus on extremes, the overarching goal of this study is to cross-examine the similarities and differences of three state-of-the-art independent products (Muti-Radar Muti-Sensor Quantitative Precipitation Estimates, MRMS; National Center for Environmental Prediction gridded gauge-only hourly precipitation product, NCEP; Integrated Multi-satellitE Retrievals for GPM, IMERG), with both traditional metrics and the Multiplicative Triple Collection (MTC) method during Hurricane Harvey and multiple Tropical Cyclones. The results reveal that: (a) the consistency of cross-examination results against traditional metrics approves the applicability of MTC in extreme events; (b) the consistency of cross-events of MTC evaluation results also suggests its robustness across individual storms; (c) all products demonstrate their capacity of capturing the spatial and temporal variability of the storm structures while also magnifying respective inherent deficiencies; (d) NCEP and IMERG likely underestimate while MRMS overestimates the storm total accumulation, especially for the 500-year return Hurricane Harvey; (e) both NCEP and IMERG underestimate extreme rainrates (>= 90 mm/h) likely due to device insensitivity or saturation while MRMS maintains robust across the rainrate range; (g) all three show inherent deficiencies in capturing the storm core of Harvey possibly due to device malfunctions with the NCEP gauges, relative low spatiotemporal resolution of IMERG, and the unusual “hot” MRMS radar signals. Given the unknown ground reference assumption of MTC, this study suggests that MRMS has the best overall performance. The similarities, differences, advantages, and deficiencies revealed in this study could guide the users for emergency response and motivate the community not only to improve the respective sensor/algorithm but also innovate multidata merging methods for one best possible product, specifically suitable for extreme storm events.

Floods in the US exhibit strong spatiotemporal variability, mainly controlled by precipitation types and catchment attributes. In a future warmer climate, it remains largely unclear how such features change, relative to the current climate. Global Climate Models at coarse resolution cannot resolve convective‐scale storms which is one of the major weather systems causing devastating floods in the US. Alternatively, coupled, high‐resolution and continental‐scale climate and flood simulations can advance our understanding. In this study, we couple a 4‐km convection‐permitting climate simulation model with a 1‐km hydrologic model to simulate the spatiotemporal variability of rainfall and flooding over the contiguous US. In particular, changes in rainfall and flood frequency, spatial scale, and seasonality are explored in major climate divisions. We found: (a) an overall increase in flood frequency (+101.7%) and spatial extent (+44.9%), mainly attributed to more extreme rainfall and variability in the future; (b) weakening rainfall and flood seasonality, resulting in more random and unpredictable events throughout the year; (c) earlier flooding season onsets in the West and snow‐dominated regions while delayed onsets in the East driven by drier antecedent soil moisture; (d) correlation between extreme rainfall and flood onsets is becoming stronger in the West yet weaker in the East in the future. Findings in this study can potentially serve as a basis for future flood exposure and risk assessments, as well as more scientific understanding of changing flood‐generating mechanisms across the CONUS.

The Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement mission (IMERG) has been widely evaluated. However, most of these studies focus on the ultimate merged satellite-gauge precipitation estimate and neglect the valuable intermediate estimates which directly guide the improvement of the IMERG product. This research aims to identify the error sources of the latest IMERG version 6 by evaluating the intermediate and ultimate precipitation estimates, and further examine the influences of regional topography and surface type on these errors. Results show that among six passive microwave (PMW) sensors, the Microwave Humidity Sounder (MHS) has outstanding comprehensive behavior, and Special Sensor Microwave Imager/Sounder (SSMIS) operates advanced at precipitation detection, while the Sounder for Atmospheric Profiling of Humidity in the Intertropics by Radiometry (SAPHIR) has the worst performance. More precipitation events are detected with larger quantitative uncertainty in low-lying places than in highlands, in urban and water body areas than in other places, and more in coastal areas than in inland regions. Infrared (IR) estimate has worse performance than PMW, and the precipitation detectability of IR is more sensitive to the factors of elevation and the distance to the coast, as larger critical successful index (CSI) over lowlands and coastal areas. PMW morphing and the mixing of PMW and IR algorithms partly reverse the conservative feature of the precipitation detection of PMW and IR estimates, resulting in higher probability of detection (POD) and false alert ratio (FAR). Finally, monthly gauge calibration improves most of the statistical indicators and reduces the influence of elevation and surface type factor on these errors.