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Weather generators have been used successfully for a wide array of applications such as hydrology, agriculture, environmental studies and recently climate change studies. Unfortunately, most weather models ignored spatial dependence exhibited by weather series at multiple sites because of climatic phenomena, which extend over a region rather than a station location and constrain the observations in a given place to be correlated to those in the surrounding area. The multi-site generation approach was then developed and has been successfully applied to precipitation occurrences and amounts. In this paper, the proposed multi-site generation approach will be used to simulate minimum and maximum temperature data. It analyzes patterns in space and investigates the dependence of weather data at multiple locations. It aims at reproducing daily spatial autocorrelations in the synthetic time series that are identical to those observed. The Peribonca River Basin in the Canadian province of Quebec was used and the results are generally satisfactory. Moreover, this multi-site approach has an important repercussion on the hydrological model compared to the uni-site approach. In order to evaluate the effects of climate changes on the Peribonca river basin hydrology, the parameters of the weather generator will be modified.