Abstract: China experiences frequent flooding due to its unique geographical and monsoon climate conditions, making it one of the most flood-prone countries in the world. This creates ongoing challenges for flood control and disaster reduction. Flood events, from historical catastrophes to recent extreme rainstorms, have persistently threatened human lives, property security, and socio-economic development. After decades of management efforts, China has established the world’s largest flood-control engineering system. The capabilities of “forecasting, pre-warning, pre-dispatching, and pre-arranged planning” and the construction of digital-twin water systems have been continuously improved, yielding remarkable achievements in flood control and disaster reduction. Both the number of flood-related fatalities and the share of direct economic losses in GDP have steadily declined. However, flood disasters in China exhibit distinct new characteristics caused by global climate change and intensified human activities. For example, the rate of warming in China exceeds the global average, leading to more frequent and intense extreme rainfall, with a northward shift in rainfall pattern; fluctuating sea-level rise intensifies flood risk in coastal areas; changes in land surfaces have further complicated hydrological processes; urban flooding, northward-moving typhoons, and disasters in small and medium-sized rivers have become key defense priorities, particularly in vulnerable regions. To address the new challenges of flooding under a changing environment, guided by the concept of “Two Upholds and Three Transformations”, it is necessary to promote scientific and technological innovation in flood control and disaster reduction, establish an integrated “space-sky-ground” monitoring system for rainfall and flooding, and improve the accuracy of monitoring and early-warning by integrating artificial intelligence and big-data technologies. Improvements in flood forecasting methods should concentrate on several key areas: model structure, data foundation, operational content, forecasting technologies, parameter determination, technical paradigms, and uncertainty representation. Additionally, there should be a shift from lumped to distributed models, from single-source to multi-source data fusion, and from deterministic to probabilistic forecasting. Finally, full-basin state awareness and intelligent deduction can be realized through digital-twin river basins, supporting the upgrading of flood control and disaster reduction toward risk-based decision-making and comprehensively enhancing flood disaster prevention capabilities.