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The occurrence of rainfall is the result of a combination of various meteorological factors. Traditional rainfall early warning models solely use Global Navigation Satellite System (GNSS)-derived Zenith Total Delay (ZTD) or Precipitable Water Vapor (PWV) to forecast rainfall, resulting in a low true detected rate. While non-linear rainfall early warning models based on the Back-Propagation Neural Network (BP-NN) algorithm consider the influences of various meteorological factors, the forecasts often exhibit a high false rate. To further improve the prediction of rainfall, a short-term rainfall early warning model based on the GNSS and BP-NN algorithms is proposed in this study. The method uses the traditional rainfall forecasting model and utilizes the BP-NN algorithm to combine various meteorological factors for rainfall early warning. The results of GNSS and BP-NN together improve the precision of rainfall early warning. Observation data from eight GNSS stations, the fifth-generation reanalysis of European Centre for Medium-Range Weather Forecast (ECMWF ERA5), and temperature, pressure, and rainfall data from corresponding meteorological stations in Ningbo, China were utilized to verify the rainfall early warning model proposed in this study. The results show that the proposed model can complement the advantages of the traditional linear and non-linear rainfall early warning methods. The model can maintain a high True Detected Rate (TDR) of rainfall early warning while simultaneously reducing the False Forecasted Rate (FFR). The average TDR of the eight GNSS stations is 100% and the FFR is 20.75%, which are both better than those of existing traditional linear and non-linear rainfall early warning models. [ABSTRACT FROM AUTHOR]