We live in a world that is increasingly vulnerable to climatic shocks. The aim of this research is to apply principal components analysis to analyze spatial variations in monthly average precipitation and monthly mean temperature data for the period of 1961-1990. The results show that the principal components 1, 2, and 3 explain 55%, 21%, and 14% of the variance in the monthly average precipitation and monthly mean temperature data. Component 1 is positively correlated with monthly average precipitation and monthly mean temperature. Component 2 is negatively correlated with monthly average precipitation and positively correlated with monthly mean temperature. This research used cluster analysis to generate 14 climatic regions in a global scale.