In recent decades, extreme events have been significantly increased over the Korean Peninsula due to climate variability and climate change. The potential changes in hydrologic cycle associated with the extreme events increase uncertainty in water resources planning and designing. For these reasons, a reliable changing point analysis is generally required to better understand regime changes in hydrologic time series at watershed scale. In this study, a hierarchical changing point analysis approach that can apply in a watershed scale is developed by combining the existing changing point analysis method and hierarchical Bayesian method. The proposed model was applied to the selected stations that have annual rainfall data longer than 40 years. The results showed that the proposed model can quantitatively detect the shift in precipitation in the middle of 1990s and identify the increase in annual precipitation compared to the several decades prior to the 1990s. Finally, we explored the changes in precipitation and sea level pressure in the context of large-scale climate anomalies using reanalysis data, for a given change point. It was concluded that the identified large-scale patterns were substantially different from each other.