As a result of increase in global average surface temperature, abnormalities in different hydroclimatic components such as evapotranspiration, stream flow and precipitation have been experienced. So investigation has to be carried out to assess the hidden abnormality subsisting in the hydroclimatological time series in the form of trend. This thesis broadly consists of following four parts. The first part comprises of a detailed review of various trend detection approaches. Approaches incorporating the effect of serial correlation for trend detection and interesting developments concerning various non parametric approaches are focused explicitly. Recent trends in annual, monthly, and seasonl (winter, pre-monsoon, monsoon and post-monsoon) Tmax and Tmin have been analyzed considering three time slots viz. 1901-2003, 1948-2003 and 1970-2003. For this purpose, time series of Tmax and Tmin of India as a whole and for seven homogeneous regions, viz. Western Himalaya (WH), Northwest (NW), Northeast (NE), North Central (NC), East coast (EC), West coast (WC) and Interior Peninsula (IP) were originally considered. During the last three decades significant upward trend in Tmin is found to be present in all regions considered either at annual or seasonal level. Sequential Mann Kendall test revealed that most of the significant upward trends both in Tmax and Tmin began after 1970. The second part discusses about numerous climate models from both Coupled Model Inter comparison Project-5 and 3 (i.e. CMIP5, CMIP3) and their skills in simulating Indian climate and assessing their performance using various evaluation measures. Performances of climate models were evaluated for whole of India and over all the individual grid points covering India. The newly defined metric symbolized as Skill_All is an intersection of the three metrics i.e. Skill_r, Skill_s and Skill_rmse, is used for overall model evaluation analysis. A notable enhancement of Skill_All for CMIP5 over CMIP3 was found. After overall model evaluation study, Compromise Programming, a distance based decision making technique, was employed to rank the GCMs gridwise. Entropy method was employed to obtain weights of the chosen indicators. Group decision making methodology was used to arrive at a consensus based on the ranking pattern obtained by individual grid points. In the third part, a detailed detection and attribution (D&A) analysis is performed to determine the causes of changes in seasonal Tmax and Tmin during the period 1950-2005. This formal D&A exercise helps in providing better insight (than trend detection analysis) into the nature of the observed seasonal temperature changes. It was noticed that the emergence of observed trend was more pronounced in Tmin compared to Tmax. Although observed changes were not solely associated with one specific causative factor, most of the changes in Tmin are above the bounds of natural internal climate variability. Finally in the fourth part, to understand the climate change impact on the hydrological cycle, a spatiotemporal change detection study of potential evapotranspiration (PET) along with Tmax and Tmin over India has been performed. Climatology patterns for PET confirmed a greater PET rate during the month of March, April, May and June. A significant increasing trend in both Tmax and Tmin (Tmin being more) was observed in more number of grid points compared to PET. Significant positive trends in Tmax, Tmin and PET were observed over most of the grid points in the IP region. Heterogeneities existed in the spatiotemporal variability of PET over all India. This spatio-temporal change detection study would be helpful for present and future water resources management.