Dynamic urban expansion simulation at regional scale is one of the important re-search methodologies in Land Use/Cover Change (LUCC) and global environmental change influenced by urbanization. However, previous studies indicate that the single urban expan-sion simulation for future scenarios at local scale cannot meet the requirements for charac-terizing and interpreting the interactive mechanisms of regional urbanization and global en-vironmental change. This study constructed a regional Dynamic Urban Expansion Model (Reg-DUEM) suitable for different scenarios by integrating the Artificial Neural Network (ANN)and Cellular Automaton (CA) model. Firstly we analyzed the temporal and spatial character-istics of urban expansion and acquired a prior knowledge rules using land use/cover change datasets of Beijing-Tianjin-Tangshan metropolitan area. The future urban expansion under different scenarios is then simulated based on a baseline model, economic models, policy models and the structural adjustment model. The results indicate that Reg-DUEM has good reliability for a non-linear expansion simulation at regional scale influenced by macro-policies.The simulating results show that future urban expansion patterns from different scenarios of the metropolitan area have the tremendous spatio-temporal differences. Future urban ex-pansion will shift quickly from Beijing metropolis to the periphery of Tianjin and Tangshan city along coastal belt.