Changes in hydrological processes caused by rapid urbanization lead to the growing incidence of urban flooding, which is a major challenge to urban sustainability. Urban floods have seriously threatened the natural environment and human life. Understanding the spatial patterns and influencing factors of urban flooding has important implications for mitigating urban flood hazards. Previous studies have demonstrated the impact of natural and human factors on urban flooding, but a comprehensive understanding of the mechanisms of urban flooding requires appropriate scales and evidence from multiple cities. In this study, 1201 flood records from 2015 to 2020 in 9 megacities (including Beijing, Tianjin, Shanghai, Xi’an, Nanjing, Wuhan, Guangzhou, Shenzhen, Shenyang) in China were used to investigate the spatial characteristics and driving factors of urban flooding. A combination of multiple stepwise regression and boosted regression tree (BRT) models was used to reveal the flood driving mechanism in megacities. The results indicated that flood events in 9 cities presented an aggregation effect, but the spatial characteristics and kernel density were different between coastal and inland cities. At all scales (1 km, 3 km, 5 km), physical geomorphic features (2D and 3D landscapes) have a high contribution to urban flooding, especially patch density, density of buildings and building shape coefficients. The relative contributions of the 2D and 3D landscape pattern factors increased by 43.8 % and 36.2 % as the grid scale increased from 1 km to 5 km, respectively. However, the relative contributions of topography, meteorology and drainage capacity factors decreased by 47.6 %, 39.0 % and 33.2 %, respectively. At a large scale (5 km), the correlation of driving factors for urban flooding was stronger, and the dominant factors were more obvious. At a small scale (1 km), the contribution of driving factors is relatively average. The scale effect significantly affects urban flooding. which suggests that an appropriate scale can more accurately capture the dominant drivers of urban flooding. Therefore, a novel method that integrates the stepwise regression model and BRT model were presented to quantify the complex relationship between urban flooding and driving factors under various analysis scales. The methods and results proposed by our study provided important insights and perspectives for urban flood risk mitigation and urban planning through multiscale analysis of flood driving mechanisms.