Abstarct: Abstract Climate change affects the intensity of river runoff, which is mainly dependent on the physical morphology, precipitation patterns, and weather conditions of a region. The GFDL-ESM2 model was selected as the most suitable climate model for hydroclimatic evaluations baxsed on MCDM techniques. The QM technique and linear scaling were used to adjust the precipitation and temperature outputs of the GFDL-ESM2 model, respectively. Result showed significant improvements in statistical indices convergence such as mean, standard deviation, and minimum and maximum values, which were brought in line with observational data. The WTC technique was used to screen the model predictors, which were then introduced as dominant inputs to the ANN-baxsed statistical downscaling model developed for precipitation, minimum, and maximum temperature parameters. Eventually, benchmark models baxsed on the satisfying amount of performance indicators like NSE, CC, and RMSE, were applied for simulating precipitation and minimum and maximum temperatures for the future period (2022-2045) under two emission scenarios (SSP245 and SSP585), These predicted precipitation and temperature values were fed into ANN-baxsed developed rainfall-runoff model to predict daily runoff values. The results showed a decrease of 5.12% and 5.65% in precipitation under the SSP245 and SSP585scenarios, respectively. Furthermore, the minimum temperature increased by 0.16 °C and 1.1 °C, and the maximum temperature increased by 0.15 °C and 0.69 °C under the SSP245 and SSP585 scenarios, respectively. Finally, the surface runoff of the Nakarud River is expected to increase by 20.7% and 8.6% under the two climate scenarios. Additionally, the physiographic characteristics of the Nakarud River basin, including slope, elevation, aspect, and drainage network density, were obtained using DEM maps, while the annual precipitation map was generated baxsed on the amount of annual rainfall. Satellite images were also used to produce soil moisture, soil texture, and land use maps by image processing and analysis methods. TOPSIS technique was used to rank these factors, and the most important factor contributing to increased runoff and flood risk in the region was identified. The results showed that the increase in runoff was mainly affected by factors such as land use, precipitation, soil moisture, slope, drainage network, soil texture, elevation, and aspect, respectively. This increase in runoff can have negative effects on various economic and environmental sectors, including an increased risk of flooding.