Solar radiation is a key component in process-based models. The amount of this energy depends on the location, time of the year, and atmospheric conditions. Several equations and models have been developed for different conditions using historical data from weather station networks or satellite measurements. However, solar radiation estimates are too local since they rely on weather stations or have a resolution that is too coarse when working with satellites. In this study, we estimated monthly global solar radiation for the south-central region of Chile using the r.sun model and validated it with observations from automatic weather stations. We analyzed the performance of global radiation results with the Hargreaves-Samani (HS) and Bristow-Campbell (BC) models. Estimates from a calibrated r.sun model accounted for 89% of the variance (r 2 = 0.89) in monthly mean values for 15 locations in the research area. The model performed very well for a wide area and conditions in Chile when we compared it with the HS and BC models. Our estimates of global solar radiation using the r.sun model could be improved through calibration of ground measurements and more precise cloudiness estimates as they become available. With additional procedures, the r.sun model could be used to provide spatial estimates of daily, weekly, monthly, and yearly solar radiation. oil and climatic conditions in the south-central region of Chile favor the production of numerous crops, including wheat, fruits, and timber. Crop productivity is highly correlated with climatic conditions (solar radiation, amount and distribution of rainfall, and temperature). This area generally has a Mediterranean climate with dry summers. However, climatic conditions can vary dramatically over short distances due to the effects of the Pacific Ocean and the Coastal and Andes Mountains (Díaz et al., 2010). To predict productivity under these conditions, climatic data are essential. Several processbased models, such as