We use a case study to show that a continuous heavy rainfall process in southern China was closely related to tropical cyclone activity in the Bay of Bengal. The continuous heavy rainfall that occurred in southern China on 11–13 May 2002 can be considered as two different processes. The first process, referred to as a predecessor rain event, occurred over southwestern China before landfall of the tropical cyclone. The second process occurred after dissipation of the tropical cyclone when its remnant caused heavy rainfall that expanded from southwestern China to the middle to lower reaches of the Yangtze–Huaihe river basin. Both of the heavy rainfall processes were closely related to the transport of warm, moist air associated with a tropical cyclone originating over the Bay of Bengal, but the mechanisms in the two processes were quite different. Low-level orographic forcing was the main contributor to the predecessor rain event, whereas baroclinic frontogenesis induced by thermal advection was the main contributor to the tropical cyclone remnant event. Both heavy rainfall events occurred beneath the equatorial entrance of the upper level East Asian subtropical jet.

Flash-flood and floodplain inundation simulations are highly sensitive to the spatiotemporal variability of convective rainfall, particularly during the initial runoff generation stage. However, coarse-resolution numerical weather prediction (NWP) forcing tends to smooth localized rainfall extremes, limiting its ability to accurately represent hydrological responses in low-relief floodplains. In this study, we couple a diffusion-enhanced radar nowcasting model, Diff_ConvLSTM, with a spatial resolution of 1 km and a temporal resolution of 6 min, to assess the hydrological value of high-resolution rainfall forcing over the middle Yangtze River floodplain. We introduce a monotone piecewise cubic Hermite interpolation scheme to ensure a stable transition from discrete high-frequency rainfall inputs to continuous hydrodynamic integration. Evaluation using a radar dataset from 2023 to 2024 shows that Diff_ConvLSTM better preserves intense convective echoes and rainband structures compared to the baseline ConvLSTM, increasing the Probability of Detection at the 40 dBZ threshold by 65.8%. A forcing-replacement experiment for the flood event on 30 June 2023 demonstrates that AI-based nowcasting rainfall forcing reduces peak-discharge underestimation, improves volumetric consistency, and produces inundation patterns that are closer to the observation-driven reference than those generated by low-resolution forecast forcing, although positive biases in inundation area and water depth persist. An additional event in 2024 confirms that the improvements are primarily reflected in discharge magnitude and flood volume representation, while enhancements in peak timing remain limited. Overall, the results illustrate both the added value and the remaining limitations of AI-enhanced nowcasting for hydrologically informed flood forecasting.