The kernel interpolation techniques provide a suitable spatial format that computes a temperature output layer for temperature output pattern. In this study, the efficiency of kernel interpolation functions for investigating spatial variability of temperature in Iran were studied during 39 years (1975-2014) at 174 stations and 29664 temperature points. In this study, six functions (Exponential, Gaussian, Guartic, Epanechnikov, Polynomial5 and Constant) were used to analyze and forecast monthly, seasonal and annual temperature kernel function patterns variability. Among the kernel functions, the strongest effect was discovered between functions for temperature forecasting the Exponential function at all stations during 39 years.  The results showed that the increases in spatial variations of the temperature were occurring mostly in mountainous regions and there are different temperature spatial variation patterns (effect factors) in Iran. In addition, the significant relationships were observed at 174 stations and 29664 points for spatial variations of temperature in Iran.

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