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The weighted mean temperature (<i>T_m</i>) is crucial for converting zenith wet delay to precipitable water vapor in global navigation satellite system meteorology. Mainstream <i>T_m</i> models have the shortcomings of poor universality and severe local accuracy loss, and they cannot reflect the nonlinear relationship between <i>T_m</i> and meteorological/spatiotemporal factors. Artificial neural network methods can effectively solve these problems. This study combines the advantages of the models that need in situ meteorological parameters and the empirical models to propose <i>T_m</i> hybrid models based on artificial neural network methods. The verification results showed that, compared with the Bevis, GPT3, and HGPT models, the root mean square errors (<i>RMSEs</i>) of the new three hybrid models were reduced by 35.3%/32.0%/31.6%, 40.8%/37.8%/37.4%, and 39.5%/36.4%/36.0%, respectively. The consistency of the new three hybrid models was more stable than the Bevis, GPT3, and HGPT models in terms of space and time. In addition, the three models occupy 99.6% less computer storage space than the GPT3 model, and the number of parameters was reduced by 99.2%. To better evaluate the improvement of hybrid models <i>T_m</i> in the precipitable water vapor (PWV) retrieval, the PWVs calculated using the radiosonde <i>T_m</i> and zenith wet delay (ZWD) were used as the reference. The <i>RMSE</i> of PWV derived from the best hybrid model’s <i>T_m</i> and the radiosonde ZWD meets the demand for meteorological research and is improved by 33.9%, 36.4%, and 37.0% compared with that of Bevis, GPT3, and HGPT models, respectively. The hypothesis testing results further verified that these improvements are significant. Therefore, these new models can be used for high-precision <i>T_m</i> estimation in China, especially in Global Navigation Satellite System (GNSS) receivers without ample storage space.