Consequently, a precise understanding of climatic patterns is essential for various planning activities, including those in the economic, agricultural, and industrial sectors.



This study employed multivariate statistical methods to identify climatic sub-regions in Central Iran. Data from 15 meteorological stations over specified statistical periods were collected and analyzed. Zoning maps were prepared using ArcGIS software.



The objective of this research was to identify climatic sub-regions in the study area using Principal Component Analysis (PCA) and Hierarchical Cluster Analysis. The use of multivariate statistical methods proved to be an effective tool for determining these climatic sub-regions. The results revealed that two factors, precipitation and maximum temperature, play the most significant role in creating climatic diversity in Central Iran.



Together, these two factors account for 73.57% of the climatic variation in the region. By performing Hierarchical Cluster Analysis using Ward's linkage method on the matrix of factor scores, four main zones and their sub-regions were identified. The findings of this study can be useful for regional and local planning in various fields, including agriculture, water resources, and urban management.