<p>Flood disasters cause severe economic losses, and understanding their spatiotemporal distribution is crucial for effective disaster prevention and mitigation. This study introduces a flood impact index (<i>FII</i>), derived from direct economic losses and the affected population, and employs a dynamic clustering framework to investigate its spatiotemporal characteristics. The framework automatically determines the optimal number of clusters for each year using a data-driven approach based on the Calinski–Harabasz (CH) index and mode analysis, and then applies hierarchical clustering with variance-based splitting and merging to ensure temporal consistency. The results indicate that (1) from 1993 to 2023, the overall impact of floods in China has exhibited a declining trend; (2) spatially, flood impacts are primarily concentrated in the central and southern regions of China, while the northern regions experience comparatively minor effects; and (3) within the study area, some regions experience either severe or minimal flood impacts and remain relatively stable, while regions with moderate flood impacts exhibit greater fluctuations. This study enhances existing spatiotemporal analysis methods and provides policymakers and planners with a robust quantitative foundation for improving flood prevention and mitigation strategies.</p>