The present study was done to study the effect of climate change on weather parameters like highest possible temperature, lowest possible temperature, average temperature and precipitation. Multiple linear Regression (MLR), Artificial Neural Network (ANN) and Statistical Downscaling Model (SDSM) models were tested in the Dal lake catchment area of Jammu and Kashmir State. Twenty seven year weather data (1985-2012) obtained from SKUAST-Kashmir weather station was used for the study. The modeling results showed a first-rate agreement between the observed data and predicted values for temperature series with high coefficient of determination R² values varying from (0.87-0.97) for different models. In case of precipitation R² values varied from (0.112-219) for different models. The low values of coefficient of determination in precipitation time series are due to lot of uncertainty in occurrence of precipitation which could not be defined by the selected models. The SDSM showed the best results of the three models tested for prediction of weather parameters. Thus SDSM was used for climate scenario generation. By comparing daily precipitation and temperature series for 1985-2012 with 2015-2030, an overall increasing pattern of 0.46%, 1.96%, 0.95% and 2.66% was observed for monthly, highest possible temperature, lowest possible temperature, average temperature and precipitation.