Analyzing the primary factors of potential evapotranspiration (PET) dynamic is fundamental to accurately estimating crop yield, evaluating environmental impacts, and understanding water and carbon cycles. Previous studies have focused on regionally average regional PET and its dominant factors. Spatial distributions of PET trends and their main causes have not been fully investigated. The Mann-Kendall test was used to determine the significance of long-term trends in PET and five meteorological factors (net radiation, wind speed, air temperature, vapor pressure deficit, relative humidity) at 56 meteorological stations in the Sichuan-Chongqing region from 1970 to 2020. Furthermore, this present study combining and quantitatively illustrated sensitivities and contributions of the meteorological factors to change in annual and seasonal PET. There was a positive trend in PET for approximately 58%, 68%, 38%, 73% and 73% of all surveyed stations at annual, spring, summer, autumn and winter, respectively. Contribution analysis exhibited that the driving factors for the PET variation varied spatially and seasonally. For stations with an upward PET trend, vapor pressure deficit was a dominant factor at all time scales. For stations with a downward PET trend, annual changes in PET mainly resulted from decreased wind speed, as did changes in spring, autumn and winter; decreasing net radiation was the dominant factor in summer. The positive effect of the vapor pressure deficit offset the negative effects of wind speed and net radiation, leading to the increasing PET in this area as a whole. Sensitivity analysis showed that net radiation and relative humidity were the two most sensitive variables for PET, followed by vapor pressure deficit in this study area. Results from the two mathematical approaches were not perfect match, because the change magnitude of the meteorological factors is also responsible for the effects of meteorological factors on PET variation to some extent. However, conducting sensitivity and contribution analysis in this study can avoid the uncertainties from using a single method and provides detailed and well-understood information for interpreting the influence of global climate change on the water cycle and improving local water management.