The purpose of this study is to estimate the precipitation amount using Spinning Enhanced Visible InfraRed Imager (SEVIRI) from Meteosat Second Generation (MSG). According to the relationship between the infrared channel brightness temperature and the properties of the clouds and the influence of these properties changes on the determination of precipitation threshold and intensity, the relationship between brightness temperature and precipitation have been studied for two stations in Hormozgan province. The performance of several regression models to estimate precipitation has been evaluated. The results showed that the exponential Gaussian process regression model has the best performance for this dataset after performing principal component analysis. Validation criteria are: The determination coefficient 0.46, the correlation coefficient of 0.68, mean absolute error of 0.83 and root mean squared error of 1.77.