It　is　widely　recognized　that　the　tourism　industry　is　susceptible　to　crisis　or　natural　disaster.　Although　some　literature　has　studied　the　consequences　of　the　crisis　and　disaster,　there　remains　a　lack　of　study　on　the　effect　of　COVID-19.　Against　this　background,　this　paper　investigates　the　tourist　flow　forecasting　by　adopting　an　advanced　nonparametric　mixed-frequency　vector　autoregressions　model　using　Bayesian　additive　regression　trees.　This　is　particularly　suitable　for　forecasting　the　presence　of　extreme　observations,　for　example,　the　COVID-19　pandemic.　We　investigate　tourism　demand　forecasting　using　a　large　number　of　predictors,　including　industrial　production　index,　CPI,　exchange　rate,　economic　policy　uncertainty,　Google　trends　index,　and　COVID-19　infection　rate.　The　data　used　for　this　study　relate　to　tourist　flows　in　Chinese　Hong　Kong,　Japan,　and　South　Korea.　Empirical　study　demonstrates　that　this　novel　model　significantly　outperforms　the　traditional　mixed-frequency　vector　autoregressions　model　to　quarterly　tourist　flow　forecasting.　Therefore,　this　model　can　significantly　enhance　tourism　forecast　accuracy　in　the　face　of　extreme　events.　This　study　contributes　to　the　literature　on　tourism　forecasting　and　provides　policymakers　with　policy　implications.