Precipitation is an important variable affecting regional climate characteristics. Accurately identifying trends in precipitation is essential for understanding the evolution of the water cycle in the context of climate change. This study uses an innovative trend analysis (ITA), an innovative polygon trend analysis (IPTA), and wavelet analysis to analyze precipitation at multiple timescales (annual, seasonal, and monthly), and the influencing factors at 30 meteorological stations in the Tai Lake Basin (TLB) from 1971 to 2018. The main conclusions are as follows: 1) the annual precipitation had a significantly increasing trend, while high precipitation had the largest increasing trend, leading to a further increase in the flood risk in the TLB. The precipitation trend mainly decreased in spring and autumn, whereas it mainly increased in summer and winter. Precipitation in different months played crucial but varying roles in the corresponding seasons; there was a sharp transition trend from August to September, whereas the transition from January to February was relatively stable. 2) There was a complex non-linear relationship between precipitation and atmospheric teleconnection. The dominant tele-correlation alone could not explain the relationship between precipitation and large-scale circulation. The highest percentage of significant power includes the optimal combination of variables meant to explain the precipitation variations. 3) The detection results from the ITA method and classic trend analysis methods (Linear regression analysis, Mann Kendall, and Modified Mann Kendall) were consistent; the non-monotonic trends masked by these methods were detectable. IPTA can systematically identify consecutive seasons and monthly transition characteristics as a supplement to ITA. This study can, therefore, provide a reference for water resource management and the prevention and control of droughts and floods.

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