Functional Data Analysis (FDA) has recently made considerable progress because of easier access to the data that are essentially in the form of curves. Although functional modeling of Iranian precipitation based on temperature or humidity was done before, here we use functional analysis of variance and covariance to analyze the weather data collected randomly from Iranian weather stations in 2010. Using a functional linear model in which the covariate (climate zones) and response variable (temperature or humidity) are functions, we estimate the coefficients via functional analysis of variance. As a result, we can determine how much of temperature or humidity variation in the weather stations is affected by the geographic areas. Using a functional analysis of covariance, we can also investigate that how much of the precipitation variation, can be expressed by the temperature residual effects or humidity residual effects (temperature or humidity effects after eliminating the climate impacts) and the corresponding climate effects.