Predicting potential distribution for alien plants by species distribution model (SDM, or Ecological Niche Model) using occurrence data and habitat environmental variables plays an important role in management of the invasive risk by an alien plant. Common groundsels (Senecio vulgaris, Asteracea), native in Eurasia and North Africa, has been a cosmopolitan weed in temperature and also listed as one of invasive plants in China. We predict the potential distribution of this species in the world and in China particularly in Maxent (maximum entropy) models by using global occurrence records of S. vulgaris and the associated climate variables. The occurrence data were collected from the online databases, Global Biodiversity Information Facility database (GBIF), Chinese Virtual Herbarium database (CVH), and also from field work in China. The climate variables were download from WorldClim (http://www.worldclim.org). The occurrence records showed that S. vulgaris is present in 16 provinces or regions in north – eastern, south – western, central and north China, and almost not present in south – eastern, north – western China. The mapping of S. vulgaris potential distribution is diagonally across China, including the north – eastern, south – western China, and the cool area between the two regions. Analysis of the contribution and importance of climatic factors in the prediction model indicated that S. vulgaris adapts to the climate in humid and cool area in China (annual mean temperature ranges 2.4 ~ 17.5 ℃, and annual precipitation ranges 550 ~ 1500 mm). It is suggested that special attention should be paid to the plain in NE China and Shandong Peninsula, Yungui Plateau, the cool mountain area around Sichuan basin, in western Hubei, southern Shaanxi, Shanxi and around Beijing in order to manage the invasion risk by S. vulgaris. The better performance of the model built by using occurrence data in China than that by using the global data in relation the predict outcome in China imply that it is might be better to use regional data than the global data when predict potential distribution for an alien plant with long invasive history in study area.