[Objective] Integrated rainfall erosivity in Jiangsu Province was evaluated at different temporal and spatial scales, and the erosion density characteristics in Jiangsu Province were explored to provide an important reference for regional rainfall erosivity prediction and soil erosion control. [Methods] This study developed a station-satellite merged calculation method for rainfall erosivity based on rainfall data from 96 meteorological stations and precipitation products of GPM IMERG and ERA5. The rainfall erosivity of Jiangsu Province from 2001 to 2023 was reconstructed, and its erosivity density and prone area division were studied . [Results] ① Method reliability was confirmed because the fusion rainfall erosivity had a higher correlation coefficient, lower deviation, and lower root mean square error than did the rainfall erosivity calculated by satellites and stations. This method captures high values of rainfall erosivity and reduces uncertainty and error. ② The mean annual rainfall erosivity in Jiangsu Province from 2001 to 2023 was 4 709.39 MJ·mm/(hm²·h·yr); the spatial distribution was 'low in the north, high in the south’. Seasonal rainfall erosivity showed a pattern of 'more in summer, less in winter’. ③ Annual rainfall erosivity in Jiangsu Province showed an increasing trend from 2001 to 2023. The climate tendency rate showed a significant increasing trend in the south of Jiangsu Province in spring, summer, and autumn, and an insignificant decreasing trend in the north, which was the opposite in winter. ④ The annual erosivity density of Jiangsu Province was 4.96 MJ/(hm²·h), and the spatial distribution was 'high in the north, low in the south’. The areas most vulnerable to rainfall erosivity were east of Xuzhou, north of Lianyungang, west of Zhenjiang, and the northern part of Nanjing City. The less vulnerable areas were Yangzhou City and Taizhou City. [Conclusion] The estimation of regional rainfall erosivity based on site-satellite precipitation merged data is reliable, reduces uncertainty and error, and improves the accuracy of satellite remote sensing applied in the field of soil erosion.