In some applications of Precipitable Water Vapor (PWV) measured by Global Navigation Satellite System (GNSS), we have to consider PWV vertical adjustments (PWVVAs) due to the height differences between the target sites and the GNSS sites. Thus, developments of global empirical models for PWVVAs deserve our attention. In this study, we found that the decrease factor of water vapor (λ) can be also used as the decrease factor of PWV when we use the Smith’s vertical variations of water vapor. Based on this conclusion, PWVVAs can be carried out by using the empirical values of λ and atmospheric total pressure (or zenith hydrostatic delay). Thus, we gave two empirical models for PWVVAs (i.e. PWVVA-Ⅰ and PWVVA-Ⅱ). On the other hand, another aim of this research is to develop an empirical model of PWVVA using neural network (i.e. PWVVANN). Measured PWV of one site, the heights of this site and the target site, and empirical values of λ and zenith hydrostatic delay were considered as the input parameters of the PWVVANN model. The output is the PWV at the target height. Global statistical results verifythat the PWVVANN model has a accuracy of 1.08 mm and its accuracy has increased by respective 30.7 %, 24.4 % and 23.6 % when compared with PWVVA-Ⅰ, PWVVA-Ⅱ and a state of the art model (i.e. the GPWV-H model).