Climate plays a key role in ecosystem services. Understanding microclimate change can be a significant help in making the right decision for ecosystems and buffering the effects of global warming. Given the large distances between meteorological stations and the changes in the climate variables within short distances, such variations cannot be detected just by using observed meteorological data. This study aimed at determining the spatial structure of the mean annual temperature, the annual average precipitation, and the climate zoning of Iran using data from 3825 stations from 2002 to 2016.The multivariate regression demonstrated the dependence of these variables on longitude, latitude, and elevation. Regression-kriging indicated a decline in temperature from east to west and northwest in high-altitude areas, while most precipitation values were observed over the Caspian Sea coastline and the Zagros Mountains. Climatic zoning showed that using auxiliary variables was very effective in detecting 24 climatic classes and understating the climate diversity in Iran. Hot to very hot and arid to very arid climate classes occupy the largest part of Iran, including the southeastern and southern desert regions. According to the generated climatic map, the large climatic diversity of Iran needs accurate policymaking regarding cultivation patterns and biodiversity. Visual comparisons of climatic zones with four remotely sensed agricultural-related variables showed that using such carefully produced climatic maps would be beneficial in classifying, assessing, and interpreting the remote sensed agricultural-related variables.