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Precipitation water vapor (PWV) is an important parameter in numerical weather forecasting and climate research. However, existing PWV adjustment models lack comprehensive consideration of seasonal and geographic factors. This study utilized the General Regression Neural Network (GRNN) algorithm and Global Navigation Satellite System (GNSS) PWV in China to construct and evaluate European Centre for Medium-Range Weather Forecasts (ECMWF) Atmospheric Reanalysis (ERA5) PWV adjustment models for various seasons and subregions based on meteorological parameters (GMPW model) and non-meteorological parameters (GFPW model). A linear model (GLPW model) was established for model accuracy comparison. The results show that: (1) taking GNSS PWV as a reference, the Bias and root mean square error (RMSE) of the GLPW, GFPW, and GMPW models are about 0/1 mm, which better weakens the systematic error of ERA5 PWV. The overall Bias of the GLPW, GFPW, and GMPW models in the Northwest (NWC), North China (NC), Tibetan Plateau (TP), and South China (SC) subregions is approximately 0 mm after adjustment. The adjusted overall RMSE of the GLPW, GFPW, and GMPW models of the four subregions are 0.81/0.71/0.62 mm, 1.15/0.95/0.77 mm, 1.66/1.26/1.05 mm, and 2.11/1.35/0.96 mm, respectively. (2) The accuracy of the three models is tested using GNSS PWV, which is not involved in the modeling. The adjusted overall RMSE of the GLPW, GFPW, and GMPW models in the four subregions are 0.89/0.85/0.83 mm, 1.61/1.58/1.27 mm, 2.11/1.75/1.68 mm and 3.65/2.48/1.79 mm, respectively. As a result, the GFPW and GMPW models have better accuracy in adjusting ERA5 PWV than the linear model GLPW. Therefore, the GFPW and GMPW models can effectively contribute to water vapor monitoring and the integration of multiple PWV datasets. [ABSTRACT FROM AUTHOR]