Abstract. The objective of this study is to develop a methodology for quantifying rainfall-induced landslide susceptibility in a regional scale. Based on the combination of mechanical stability analysis and artificial neural network (ANN) and of Geographic Information Systems (GIS) and detailed field investigation, the methodology was applied to the new urban area of Fengjie County in Northeastern Chongqing, China. According to the field investigation, an analysis sample database (ASD) pertaining to 6 slope stability influencing factors was built by means of uniform design method, and 30 samples for slope stability analysis were grouped. Then, safety factors of the sample groups were calculated by means of Geo-studio software concerning rainfall infiltration into slopes. To obtain overall slope stability analyses in the study area, the ANN was employed and the safety factors of the samples were utilized as training samples by ANN. Combining the trained ANN and survey data of the study area, the computation of safety factors under different rainfall were integrated and mapped within the GIS. The landslide susceptibility assessment indicates that slopes in more than a quarter of the study area are prone to landslides under rainstorm and severe rainstorm, however, slopes in the whole area under light rainfall, moderate rainfall and even heavy rainfall are relatively safer. Further, the results highlight the geological settings effect on landslide susceptibility as the high susceptibility zones are mainly distributed along the Yangtze River and its three branches, where the bank slopes are composed of fractured stratum, weak rocks and deposits. In good accordance with the rainfall-induced landslide events occurred in recent years and some findings in other literature about the study area, it is proved that the methodology presented in this paper could reasonably delineate landslide susceptibility under rainfall.