The increasing volume of climate data from observations, analysis products, and climate model output presents the climate science community with unprecedented data analysis challenges and opportunities. This challenge becomes greater when targeting extreme events as standard data reduction techniques like multi-model ensemble averaging reduce the magnitude of extremes. In this talk, we will focus on evaluation of regional climate model output using various statistical techniques considering spatial and temporal features. The validation data are from gridded data sets base on observations, and reanalysis data. These work are all joint work with many scientists from MCS and U. Chicago. First, we have developed spatio-temporal correlations to answer a question --- whether high resolution climate modeling adds value on top of coarse resolution simulation. We have also developed a new algorithm to track the intensity, frequency, duration of rainstorms, using which we looked at fut ure changes in rainstorms and added value by high resolution simulation (with convection permitted). Second, we have developed a simple GEV model and a robust approach to estimate uncertainty of the GEV parameters. We also explore the effects of block length for block-maxima approach. Third, we looked at internal variability of regional climate modeling and demonstrated the robustness of various number of ensemble members using resampling technique. Last, we have initiated a new project using machine learning techniques to learn the parameterizations in the climate model. This will give us opportunities to replace the very time-consuming modules with machine learning algorithms. We are also developing hydrological modeling at very high resolution to look at climate change impacts on streamflow and related water resource over entire CONUS.