The dimensionless Leaf Area Index (LAI) is widely used to characterize vegetation cover. With recent remote sensing developments LAI is available for large areas, although not continuous. However, in practice, continuous spatial-temporal LAI datasets are required for many environmental models. We investigate the relationship between LAI and climatic variable rainfall and Growing Degree Days (GDD) on the basis of data of a cold semi-arid region in Southwest Iran. For this purpose, monthly rainfall and temperature data were collected from ground stations between 2003 and 2015; LAI data were obtained from MODIS for the same period. The best relationship for predicting the monthly LAI values was selected from a set of single- and two-variable candidate models by considering their statistical goodness of fit (correlation coefficients, Nash-Sutcliffe coefficients, Root Mean Square Error and mean absolute error). Although various forms of linear and nonlinear relationships were tested, none showed a statistically meaningful relationship between LAI and rainfall for the study area. However, a two-variable nonlinear function was selected based on an iterative procedure linking rainfall and GDD to the expected LAI. By taking advantage of map algebra tools, this relationship can be used to predict missing LAI data for time series simulations. It is also concluded that the relationship between MODIS LAI and modeled LAI on basis of climatic variables shows a higher correlation for the wet season than for dry season.