In this study, various wavelet analysis methods are used to investigate possible influences of large-scale climate patterns, such as El Niño-Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), Pacific Decadal Oscillation (PDO), and Indian Ocean Dipole (IOD), on precipitation extremes over Beijing-Tianjin-Hebei region in China at different time scales. Firstly, the temporal patterns of precipitation extremes are detected by wavelet transform. Significant annual or inter-annual oscillations for the precipitation extremes during 1958–2017, with periodicities of around 0.5–1 year, 1–2 years, and 2–5 years were being found for monthly, seasonal, and annual time series, respectively. Subsequently, wavelet coherence method is used to identify the dominant driving factors of precipitation extremes, with ENSO, IOD, and NAO showing stronger correlations with monthly, seasonal, and annual precipitation extremes, respectively. Meanwhile, partial wavelet coherence analyses indicate that the standalone influences of climate factors may be weak, and the influences seem to be stronger because of their interdependences on other climate indices. Finally, multiple wavelet coherences reveal that variations of precipitation extremes could be better explained by combinations of two or more factors, although the additional explanatory variable may have not a significant increase in percent number of significant coherence.