Abstract: The generalized Pareto distribution (GPD) is a good probability distribution model to describe peak-over-threshold (POT) series, but the determination of its threshold is a great challenge in practical application. To solve this problem, we herein describe a Pickands bootstrap moment method for estimation of the GPD parameters. The method is used to estimate the shape parameter of GPD and the optimum number of POT and calculate the threshold, then the scale parameter is obtained with the moment estimation method. This new method is applied to the daily precipitation series of the Beijing meteorological station and its results are compared with those estimated by the linear moment estimation method using the same threshold. The comparison shows that it has a good ability of parameter estimation for GDP and generalized extreme value (GEV). From the fitting of the GDP quantiles and POT series, their correlation coefficients are up to 0.997 or more while their standard deviation and maximum deviation are the lowest. These results indicate that the Pickands bootstrap moment estimation method is obviously superior to the linear moment estimation.