Due to the important role of climatic parameters such as radiation, temperature, precipitation and evaporation rate in water resources management, this study employed time series modeling to forecast climatic parameters. After normality test of the parameters, nonparametric Mann-Kendall test was used in order to do trend analysis of data at P-value<0.05. Relative humidity and evaporation (with significant trend, -0.348 and -0.42 cm, respectively), as well as air temperature, wind speed, and sunshine were selected for time series modeling. Considering the Autocorrelation function (ACF) and Partial Autocorrelation function (PACF) and trend of data, appropriate models were fitted. The significance of the parameters of the selected models was examined by SE and t statistics, and both stationarity and invertibility conditions of Autoregressive (AR) and Moving average (MA) were also tested. Then, model calibration was carried out using Kolmogorov-Smirnov, Anderson- Darling and Rayan-Joiner. The selected ARIMA models are ARIMA(0,0,11)*(0,0,1), ARIMA(2,0,4)*(1,1,0), ARIMA(4,0,0)*(0,1,1), ARIMA (1,0,1)*(0,1,1), ARIMA (1,0,0)*(0,1,1) for relative humidity, evaporation, air temperature, wind speed and sunshine, respectively. The fitted models were then used to forecast the parameters. Finally, trend analysis of forecasted data was done in order to investigate the climate change. This study emphasizes efficiency of time series modeling in water resources studies in order to forecast climatic parameters.