Climate regionalization is essential for characterizing spatial and temporal climatic variability, producing meteorological forecasts, analyzing trends at different scales and, determining the climatic impact on human activities. The aim was to propose a climatic regionalization for Santa Cruz province, based on gridded data of rainfall and temperature (period 1995 to 2014), and subsequent characterization. To achieve this goal, we applied the non-hierarchical k-means clustering method to monthly accumulated rainfall and monthly average temperature databases. The Thornthwaite classification modified by Feddema was used to classify each cluster. Results from this study showed that Santa Cruz province is divided into 11 climatic regions based on rainfall and temperature. The driest and warmest regions are located in the center and northeast of the province and the most humid and coldest ones in the south and southwest. Regionalization is an important component of many applied climate studies and it can be used in other studies related to agriculture, energy production, water resource management, extreme weather events, and climate change, among others. This regionalization in particular can be used to examine the impacts of climate change in regional studies of climatic scale reduction in Santa Cruz province. As well as this tool can be essential in the study of drought and its impacts and contributes to a better understanding of the climatic phenomena that condition drought.