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Structural changes often occur in air passengers due to some external factors such as airport expansion, policy orientation and economic development; model uncertainty is a common long-standing issue in forecasting. To address these issues, a novel time-varying Jackknife model averaging method (TVJMA) (Sun et al, 2020, 2012) is employed to predict air passengers of the Top 5 airports in China. Based on nonparametric estimation, the optimal time-varying weights for various candidate models with time-varying parameters in candidate models are obtained by minimizing the local Jackknife criterion at every time point t. TVJMA method allows the weights and parameters to change over time. Empirical results show that the TVJMA method used in this paper is significantly superior to other benchmark models, including Hansen and Racine's (2012) Jackknife model averaging method (JMA), autoregression model (AR), autoregression integrated moving average model (ARIMA), seasonal autoregression integrated moving average model (SARIMA), and time-varying parameter model (TVP). Furthermore, the predictive effect of TVJMA is robust to different test sets and prediction steps. Overall, TVJMA method effectively reduces the predictive risk caused by structural changes and model uncertainty, and thus produces accurate and stable forecasts of air passengers. [ABSTRACT FROM AUTHOR]