Given the rapidly changing climate, accurate spatiotemporal information on the evolution of extreme rainfall events is required for flood risk assessment and the design of resilient infrastructure. Consequently, various research efforts have focused on investigating the appropriateness of various parametric and non‐parametric approaches in modeling the observed changes in the frequency of extreme rainfall over time. Yet, the assumption of stationarity, or the change of model parameters when accounting for nonstationary rainfall, may magnify estimation uncertainty of rain rates associated with low exceedance probabilities. Moreover, the use of climate model results may yield inconclusive outcomes, given the existence of epistemic uncertainties in the frequency of extreme events developing on smaller spatial scales or over complex terrain. Herein, we employ a parametric approach based on multifractal scaling arguments, along with high‐resolution (4‐km) hourly precipitation estimates covering a 40‐year period over CONUS, to derive Intensity‐Duration‐Frequency curves and investigate the spatiotemporal evolution of extreme rainfall over a wide range of characteristic temporal scales and exceedance probability levels. Considering the robustness of the multifractal models even when fitted to short rainfall records, we uniquely apply the framework to sequential 10‐year segments of data, where the rainfall process can be reasonably assumed stationary. The obtained results reveal that existing infrastructure may be severely impacted by the intensification of precipitation extremes due to climate change, with the observed trends being significantly influenced by the topography and rainfall climatology of each region, while depending on the averaging durations and return periods of interest.

The spatiotemporal character of rainfall is particularly important for hydrologic modeling, as well as hydroclimatic risk estimation and impact assessment. Existing atmospheric reanalysis datasets offer extensive record lengths and global coverage, but usually their spatial resolution is coarse for distributed hydrologic simulations at small spatial scales. On the other hand, the temporal coverage of high‐resolution radar‐based rainfall estimates can be rather short for risk applications. To address these shortcomings, we simultaneously bias‐correct and downscale a state‐of‐the‐art atmospheric reanalysis (ERA5) rainfall dataset, using the radar‐based Stage IV precipitation product as fine resolution reference, to develop an hourly CONUS‐wide precipitation product over a 4‐km grid, which extends back to 1979. In this regard, we refine an existing parametric quantile mapping framework based on a two‐component theoretical distribution model, where we impose continuity of the parametric forms via optimal threshold selection to transition between higher and lower rain rates. An evaluation over the probability frequency and time domains, using NOAA’s raingauge measurements as benchmark, reveals that the developed product benefits from the strengths of the calibration datasets, demonstrating good performance and robust behavior over all studied time periods and Köppen climate classification zones, including snow‐prone regions or areas where mesoscale convective systems become dominant. The accuracy of the yielded high spatial‐resolution rain rates, especially in low probability events, shows that the developed product can be effectively used for hydroclimatic risk applications and frequency analysis, while its high temporal and spatial resolution makes it particularly useful for distributed hydrologic modeling.

In view of Ethiopia’s significant renewable energy (RE) potential and the dynamic interactions among the components of the Water–Energy–Food (WEF) Nexus, we attempted to incorporate solar and small-scale hydropower into the optimal design of an environmentally friendly microgrid with the primary goal of ensuring the sustainability of irrigation water pumping, while taking advantage of existing infrastructure in various small administrative units (kebele). Any additional generated energy would be made available to the community for other needs, such as lighting and cooking, to support health and food security and improve the general quality of life. The novelty of the study stems from the utilization of in situ social data, retrieved during fieldwork interviews conducted in the kebele of interest, to ascertain the actual needs and habits of the local people. Based on these combined efforts, we were able to formulate a realistic energy demand plan for climatic conditions typical of Sub-Saharan Africa agricultural communities and analyze four different scenarios of the microgrid’s potential functionality and capital cost, given different tolerance levels of scheduled outages. We demonstrated that the RE-based microgrid would be socially and environmentally beneficial and its capital cost sensitive to the incorporation of individual or communal machines and appliances. Ultimately, the social impact investigation revealed the design would be welcomed by the local community, whose members already implement tailor-made solutions to support their agricultural activities. Finally, we argue that extended educational programs and unambiguous policies should be in place before any implementation to ensure the venture’s sustainability and functionality.