Abstract: Urban flooding triggered by extreme rainfall is a major disaster risk under global climate change. However, current research rarely conducts urban inundation analysis at the watershed scale. To explore the feasibility of hydrological-hydraulic coupled models for inundation reconstruction in urban watersheds, this study established a coupled framework of the WRF-Hydro distributed hydrological model and the HEC-RAS 2D hydraulic model within the Wenyu River Basin, an urban watershed in the Beijing-Tianjin-Hebei region. This framework was applied to reconstruct flood inundation during the ‘23·7’ once-in-a-century extreme rainfall event, driven by both CLDAS rainfall and WRF forecast rainfall. The results are summarized as follows: (1) The ‘23·7’ extreme rainfall event exhibited significant spatial variability. The maximum cumulative rainfall reached 590.70 mm, with a Precipitation Concentration Index (PCI) of 3.4 and a Coefficient of Variation (CV) of 1.51. This heavy rainfall was primarily concentrated in the upstream areas of tributaries such as the Dongsha River and Nansha River, while urban areas experienced relatively lower cumulative rainfall. This spatiotemporal analysis of rainfall-runoff, combined with the river network, indicates that urban inundation in the Wenyu River Basin primarily originated from upstream flood discharge. This finding underscores the critical role of watershed hydrological connectivity in the formation of urban floods. (2) Overall, the WRF-Hydro model, driven by both CLDAS and WRF rainfall, successfully simulated the flood processes. Flood simulation performance was better when driven by CLDAS rainfall. However, even with WRF forecast rainfall, the model achieved an NSE of 0.75, a runoff ratio (RR) of 0.87, and a relative error (RE) of −22.95%. The peak time errors were consistently within 1–2 hours, which is superior to the peak time error range reported in similar urban watershed studies. This demonstrates the model's capability for accurate flood forecasting even with forecast rainfall data. (3) The coupled WRF-Hydro hydrological model and HEC-RAS hydrodynamic model successfully reproduced the flood inundation process in the urban watershed from 00:00 on July 31, 2023, to 23:00 on August 5, 2023, under both rainfall scenarios. Validation against SAR data at 08:00 on August 5 showed that both accuracy and F1 scores exceeded 0.7. Notably, in the Dongsha River Riverside Forest Park area, both accuracy and F1 scores exceeded 0.88, indicating excellent local simulation performance. The phenomenon in which the rate of increase in inundation area during the recession phase exceeded that during the rising phase reflects a critical contradiction between rapid runoff generation caused by extreme rainfall and limited drainage capacity in urban areas with a high proportion of impervious surfaces. (4) The WRF-Hydro and HEC-RAS hydrological-hydraulic coupled model constructed in this study demonstrates significant potential for urban watershed flood simulation. Regarding its transferability to other urban watersheds, a key advantage of this study is its reduced reliance on highly detailed data. By generalizing pipe networks using Manning’s parameters, the model can provide rapid, macroscopic predictions of flood inundation extent and trends, making it particularly suitable for data-scarce environments or emergency response scenarios. This approach offers a practical solution for rapid flood assessment and early warning in diverse urban settings. In conclusion, this study provides new insights for optimizing urban drainage systems and formulating flood control measures to address the aforementioned contradiction. The successful application of the WRF-Hydro and HEC-RAS coupled framework offers a valuable tool for urban flood risk assessment and management, particularly in the context of increasing extreme rainfall under climate change. The findings underscore the need for integrated approaches that consider both hydrological and hydraulic processes at the watershed scale to enhance urban resilience to flooding.