The pelagic species is closely related to the marine environmental factors, and establishment of forecasting model of fishing ground with high accuracy is an important content for pelagic fishery. The chub mackerel(Scomber japonicus) in the Yellow Sea and East China Sea is an important fishing target for Chinese lighting purse seine fishery. Based on the fishery data from China’s mainland large-type lighting purse seine fishery for chub mackerel during the period of 2003 to 2010 and the environmental data including sea surface temperature(SST), gradient of the sea surface temperature(GSST), sea surface height(SSH) and geostrophic velocity(GV), we attempt to establish one new forecasting model of fishing ground based on boosted regression trees. In this study, the fishing areas with fishing effort is considered as one fishing ground, and the areas with no fishing ground are randomly selected from a background field, in which the fishing areas have no records in the logbooks. The performance of the forecasting model of fishing ground is evaluated with the testing data from the actual fishing data in 2011. The results show that the forecasting model of fishing ground has a high prediction performance, and the area under receiver operating curve(AUC) attains 0.897. The predicted fishing grounds are coincided with the actual fishing locations in 2011, and the movement route is also the same as the shift of fishing vessels, which indicates that this forecasting model based on the boosted regression trees can be used to effectively forecast the fishing ground of chub mackerel in the Yellow Sea and East China Sea. The pelagic species is closely related to the marine environmental factors, and establishment of forecasting model of fishing ground with high accuracy is an important content for pelagic fishery. The chub mackerel (Scomber japonicus) in the Yellow Sea and East China Sea is an important fishing target for Chinese lighting purse seine fishery. Based on the fishery data from China’s large mainland-type lighting purse seine fishery for chub mackerel during the period of 2003 to 2010 and the environmental data including sea surface temperature (SST), gradient of the sea surface temperature (GSST), sea surface height (SSH) and geostrophic velocity (GV), we attempt to establish one new forecasting model of fishing ground based on boosted regression trees. In this study, the fishing areas with fishing effort is considered as one fishing ground, and the areas with no fishing ground are randomly selected from a background field, in which the fishing areas have no records in the logbooks. The perform ance of the forecasting model of fishing ground is evaluated with the testing data from the actual fishing data in 2011. The results show that the forecasting model of fishing ground has a high prediction performance, and the area under receiver operating curve (AUC) attains 0.897 The predicted fishing grounds are coincided with the actual fishing locations in 2011, and the movement route is also the same as the shift of fishing vessels, which indicates that this forecasting model based on the boosted regression trees can be used effectively to forecast the fishing ground of chub mackerel in the Yellow Sea and East China Sea.