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Extreme precipitation index (EPI) is used to assess the extreme precipitation events, to further monitor and detect climate change impacts. The primary objective of the paper is to assess the extreme precipitation events and their spatiotemporal variability of 14 EPIs, and further investigate the linear and nonlinear relationships between typical EPIs and global climate anomalies El Nino Southern Oscillation (ENSO), using Nino 3.4 and SOI as climate indices, for the period of 1969-2016 in the Wei River Basin (WRB) of China. Observed daily precipitation data from 18 meteorological stations in the WRB were selected to calculate 14 EPIs derived or modified from core indices of the CCl/WCRP Expert Team for Climate Change Detection Monitoring and Indices (ETCCDMI). The trends of EPIs were estimated using Mann-Kendall non-parametric test. The teleconnections of ENSO to EPIs were revealed using principal component analysis (PCA), Pearson's correlation and wavelet analysis. Results indicate that: (1) The change patterns of extreme precipitation events varied for different EPIs and different meteorological stations for the period of 1969-2016 in the WRB. Four EPIs were negative trends dominated, and six EPIs were positive trends dominated, indicating that the extreme precipitation events had mainly increased for the past 48 years. (2) The spatial distributions of seven categories of trends were scattered and irregular for different EPIs and different areas of WRB. However, some of the EPIs had similar spatial distributions of trends. For example, the stations that had increasing trends mainly located at the northern part of WRB, especially in the Jing River Basin (JRB), for six precipitation intensity EPIs except R150, which was stationary trend dominated. (3) The nonlinear teleconnection relationships between typical EPIs and two climate anomalies revealed using wavelet analysis were consistent with linear relationships using correlation analysis. The Nino 3.4 and SOI had similar wavelet coherence but opposite phase difference with PC1 of typical EPIs. The phase differences between Nino 3.4 and PC1 of EPIs were anti-phase dominated, and in-phase dominated between SOI and PC1 of EPIs, indicating that the Nino 3.4 exerted negative climate impacts on the extreme precipitation events in the WRB and vice-versa for SOI. (4) The results should be beneficial to assess the extreme precipitation events, which provide references for the water resources planning and management in the WRB. It is possible to estimate and predict the future extreme precipitation events using climate models and global climate anomalies as potential predictors, which should be further studied in our future work.