Abstarct: Several factors, including natural and human-induced, can affect river discharge. This study aims to examine the influence of land use changes and climate change on the monthly average rainfall time series in the Talar River Basin, situated in northern Iran. To investigate the impact of human factors, namely land use change and point source operations, on monthly average streamflow, the DBEST method was used to detect any breakpoint in the streamflow time series caused by gradual changes in land use and climate. The SWAT model was used to simulate the basin at two stations, Kiakola and Shirghah, between 2001 and 2020. The land use maps were created for the years 2001 and 2019. To run the model, the input data set included digital elevation model (DEM), stream network (if available), land use maps, soil map, hydrological and meteorological data, and point water harvesting parameters and output data. To extract land use maps, was created by combining Landsat 5 and 8 and Modis satellites for the years 2001 and 2019. Calibration and validation at the Kiakola station showed that the Nash–Sutcliffe model (NSE) had an efficiency of 0.8 and 0.76, respectively, while at the Shirghah station, the same values were 0.84 and 0.75. Findings revealed that human activities, specifically the combined impact of land use change and point source operations, had a 60% influence on the monthly average streamflow of the Talar River. Further showed that the combination of land use and harvesting played the most significant role in the basin’s outflow on a monthly scale. This study aimed to determine how climate change would impact streamflow in the Talar basin between 2020 and 2050. This research analyzed databaxse on the CMIP6 climate change scenarios, as well as land use projections for 2035 and 2050. After calibration and validation of the model using best parameters from 2001–2020, CMIP6 data was downscaled baxsed on six models and under two scenarios, SSP2-4.5 and SSP5-8.5 were projected. To downscale atmospheric general circulation models, two methods were utilized: the Delta method (Delta) and Quantile mapping (Qm). The best values of the were obtained from the INM model. In addition, the Delta method for the downscaling precipitation and the Qm method for downscaling temperature showed better efficiency values. For example, the values of RMSE, NRMSE and MAE for the rainfall of Kiakla station are 2.185, 0.0402 and 1.716, respectively, using the Delta method. In this research, the CA-Markov prediction model was used to simulate and predict land-use change for 2035 and 2050. The precipitation and temperature data from climate change scenarios and land use maps was imported to the SWAT model to predict mean monthly streamflow during 2020-2035 and 2020-2050. The results indicated that there was a decrease in average monthly streamflow due to changes in land use, such as the expansion of urban areas and a reduction in agricultural land. In the future, changes in land use and land cover (LULC) may impact streamflow.