Precipitation is the main source of surface freshwater. The temporal and spatial heterogeneity of precipitation distribution brings great uncertainty to the study of the surface water cycle. The study of spatial heterogeneity and the factors influencing precipitation has always been the focus of water cycle research. To explore the relationship between the spatial distribution of precipitation and terrain factors， 351 precipitation observation stations in eastern China with abundant observational data were used according to the Ordinary Least Squares regression （OLS）， Geographically Weighted Regression model （GWR）， and Multi-scale Geographically Weighted Regression model （MGWR）. The results show that OLS cannot show the influence of terrain factors on the spatial heterogeneity of precipitation distribution， while GWR and MGWR achieved a better goodness of fit and stronger interpretability （Goodness of fit R²>0.7）. Furthermore， the MGWR can reflect the scale effects of terrain factors on the spatial distribution of precipitation based on bandwidths， and local influencing factors with smaller bandwidths have a stronger influence on the spatial heterogeneity of precipitation. For the annual average precipitation， the terrain elevation and terrain relief are the main terrain factors that affect the spatial heterogeneity of precipitation， while the terrain slope and Prevailing Wind-direction Effect Index （PWEI） have no significant impact on precipitation. However， seasonally， the influence of different terrain factors on the spatial distribution of precipitation is different. Specifically， in summer， terrain elevation is more important than other factors； in spring and autumn， the distance from the coast plays an important role in the mountain regions， and in winter local influencing factors such as terrain relief mainly affect the spatial distribution of precipitation. Clarifying the relationship between precipitation and terrain factors can help us understand the contribution of complex terrain factors （in all seasons） to precipitation and provide support for model simulation and improvement in regions with a complex topography.