Abstract: A daily precipitation dataset, for the period from 1905 to 2007 in Nanjing is constructed. Firstly, annual maximum of daily rainfall (AMDR) are modeled by using the generalized extreme value (GEV) distribution from the extreme value theory to describe and predict extreme value of future behavior. We estimate model parameters by the MLE method and evaluate the confidence level with the profile log likelihood function. Meanwhile, the diagnosis of model's rationality through 4 kinds of visual illustration is made with the result that the Frechet distribution of GEV fits the extreme daily precipitation best. Second, the scope of application of the generalized Pareto distribution (GPD) based on three time series scenarios is studied and a detailed approach how to gather useful extreme information for a given threshold is discussed emphatically. The results show that regardless of length of climate time series, the critical threshold of daily precipitation of 24 mm assumed is appropriate for GPD analysis. This threshold is lacated near the 91th percentile of annual precipitation series, i.e. above 91% of the sample capacity in the 50 years daily observational data is able to meet the requirenment to analyze extreme daily precipitation with the GPD. According to statistical inference of extreme values through the GEV and GPD, it is concluded that the confidence level of GPD is higher than that of the GEV, i.e. with less uncertainty and thus more suitable for climate time series analysis for China where the sample capacity of climate data is not large at the moment yet. We also set additional variables to replace shape and scale parameters in the GPD model and esp. introduce dynamic variables to describe the linear change in order to analyse the variation of precipitation series and its possible influence on the extreme value distribution. This kind of variation includes changes in the longrange mean and the percentile of precipitation series. However, no distinct interfere caused by these variations is found with the analyses.