Abstract: The study of the spatial-temporal distribution and driving factors of atmospheric PM2.5 in Jiangsu Province is of great significance for formulating effective prevention and control policies. Using various statistical methods, geographical detector, and a multi-scale geographically weighted regression (MGWR) model, this research explored the spatiotemporal distribution characteristics of PM2.5 in Jiangsu Province and its influencing factors. The results indicated that: (1) The annual average PM2.5 concentration ranged from 33.93 to 70.65 μg·m-3, showing an upward trend in 2000, a decline after 2013, and an overall downward trend. Seasonally, PM2.5 concentrations were highest in winter, followed by spring and autumn, and lowest in summer. The multi-year average spatial distribution of PM2.5 showed higher concentrations in the northwest and lower concentrations in the southeast. (2) Factor detection results indicated that humidity and wind speed were the primary driving factors influencing the spatial differentiation of PM2.5. Furthermore, the explanatory power of all driving factors was greater than that of any single factor, suggesting that spatial differentiation of PM2.5 was the result of a combination of multiple driving factors. (3) The scale and intensity of the effects of each driving factor on PM2.5 showed significant differences. Sunshine duration had the smallest effect scale and the greatest spatial heterogeneity. Elevation and annual average relative humidity had the largest effect scales, showing a relatively consistent influence on PM2.5 concentration across the study area. PM2.5 concentration was positively correlated with elevation and annual average temperature, and generally negatively correlated with annual average relative humidity, wind speed, precipitation, and sunshine duration.