Artificial rainfall simulations by using stochastic models provide a wider range of potential meteorological scenarios compared to historical observational data, enabling a more comprehensive assessment of potential water resource challenges. The hidden Markov model framework for simulating hourly rainfall is capable to capture essential characteristics of daily precipitation, including dry periods and droughts, seasonal and temporal variations in occurrence and intensity, as well as tendencies towards extreme values. This model incorporates several innovations compared to conventional methods, including three(Dry, Wet& Wetter) and clone states for dry periods and temporal non-homogeneity in the transition matrix. It is set up in a Bayesian framework that allows for quantification of parametric and predictive uncertainty, allowing for full model evaluation through posterior predictive analyses. For this research, eight years of hourly rainfall data (from 2015 to 2022) from the Amir Kabir station in Alborz province were utilized. The results of the model are interpretable and allow for the examination of seasonal and annual variations in hourly rainfall occurrence and intensity. Considering the various complex aspects of rainfall patterns at an hourly time scale, this model can serve as a valuable tool for meteorologists, hydrologists, and water resource planners.