The Hidden Markov Model (HMM) failed to consider external factors that influence the dynamic changes in rainfall. Therefore, the Bayesian Non-Homogeneous Hidden Markov Model (Bayesian-NHMM) was introduced in this study to account for such factors, including seasonal rainfall characteristics and large-scale atmospheric circulation factors. Daily rainfall during the rainy season in the Beijing-Tianjin-Hebei region from 1979 to 2018 was simulated and predicted, considering the impacts of several large-scale atmospheric circulation factors, such as the East Asian Summer Monsoon Index, the Pacific Decadal Oscillation, the Indian Ocean Dipole, the North Atlantic Oscillation, the El Ni?o Index, and the Southern Oscillation Index (SOI). Additionally, seasonal rainfall characteristics were introduced for the first time to represent the intra-annual variation in daily rainfall during the rainy season in this region. The results indicated that: (1) The Bayesian-NHMM model outperformed the traditional HMM model; (2) Seasonal rainfall characteristics and SOI were identified as the main external factors influencing the dynamic changes of the daily rainfall in the Beijing-Tianjin-Hebei region; (3) After incorporating the seasonal rainfall characteristics, Bayesian-NHMM model is able to identify the seasonal variation patterns of daily rainfall in the region more effectively. Bayesian-NHMM model can significantly improve the accuracy of daily rainfall simulation and prediction in the Beijing-Tianjin-Hebei region and can be easily applied to other river basins and urban agglomerations in China.