Extreme rainstorms are increasingly threatening urban flood safety under climate change and rapid urbanization. Because rainfall is a direct driver of urban flooding, existing studies usually describe flood-producing rainstorms using meteorological indicators such as rainfall amount, duration, intensity, and coverage. However, in complex urban catchments, rainfall spatiotemporal heterogeneity interacts with highly variable underlying surface and drainage conditions, making meteorological indicators alone insufficient to explain flood responses. Current studies still insufficiently account for the hydrological properties of rainfall areas and the effects of observation spatial resolution on rainfall characterization. Therefore, we develop a hydrometeorological-integrated method for quantifying the spatiotemporal characteristics of rainfall and combine rainfall upscaling with urban flood simulation to examine how rainfall characteristics and underlying surface conditions jointly affect flood processes while identifying key features of urban flood-producing rainstorms.

The Qing River basin in Beijing, a typical highly urbanized catchment of approximately 200 km² characterized by fragmented impervious surfaces and dense drainage networks, was selected as the study area. Rainfall fields were obtained from two X-band dual-polarization weather radars with a temporal resolution of 3 min and an original spatial resolution of 75 m. Radar quantitative precipitation estimation was conducted based on specific differential phase information and evaluated using observations from meteorological stations. The rainfall product was converted into a 100 m regular grid and spatially upscaled to 300 m, 500 m, 1 km, 3 km, and 10 km resolutions. Seven representative rainfall events with different rainfall amounts, intensities, movement paths, and spatial patterns were selected to construct rainfall scenarios. A hydrometeorological-integrated method incorporating eight temporal indicators related to rainfall duration, amount, intensity, and variability, along with eight spatial indicators related to rainfall spatial variability, impervious-area rainfall, rainstorm coverage, and rainfall-area distance to drainage elements, was established to quantify the spatiotemporal characteristics of rainfall. Urban flood processes were simulated under different rainfall scenarios using a grid-based urban hydrological model that represents runoff generation and routing over pervious areas, impervious areas, drainage pipes, and river channels. Peak flow bias and high-flow accuracy were used to evaluate flood impacts, whereas correlation and random forest importance analyses were used to identify the key rainfall characteristics controlling urban flood responses.

The results show that decreasing rainfall spatial resolution considerably changed the expression of rainfall spatiotemporal characteristics. When the resolution was decreased from 100 m to 10 km, rainfall amount, rainfall intensity, and spatiotemporal variability were generally underestimated, rainfall duration was prolonged, and rainfall coverage and rainfall-area distances generally increased. At the 10 km resolution, the median underestimation of peak rainfall intensity reached approximately 30%, and rainfall spatial variability decreased by approximately 40%. These changes further affected urban flood simulations and resulted in peak flow biases of up to −40%. Random forest analysis showed that temporal rainfall characteristics contributed approximately 64% and 70% to peak- and high-flow formations, respectively, indicating their dominant role in shaping urban flood responses. Among all indicators, the maximum 30-min rainfall intensity had the greatest influence on flood peaks, with an importance score close to 30%. Meanwhile, spatial indicators such as rainfall-area distance and rainfall over impervious areas had non-negligible effects, with importance scores of approximately 10%.

In this study, we develop a hydrometeorological-integrated method for quantifying rainfall spatiotemporal characteristics and identifying urban flood-producing rainstorms. Results show that rainfall observation resolution directly affects rainfall intensity, variability, coverage, and location characteristics, thereby considerably affecting urban flood simulations. Rainfall intensity, particularly the maximum 30-min intensity, dominates flood peaks and high-flow processes, whereas rainfall-area hydrological properties and distances to drainage systems remain critical. The proposed framework extends rainfall characterization beyond conventional meteorological indicators and supports more accurate rainfall monitoring and urban flood prediction.