This　article　is　concerned　with　feature　screening　for　varying　coefficient　models　with　ultrahigh-dimensional　predictors.　We　propose　a　new　sure　independence　screening　method　based　on　quantile　partial　correlation　(QPC-SIS),　which　is　quite　robust　against　outliers　and　heavy-tailed　distributions.　Then　we　establish　the　sure　screening　property　for　the　QPC-SIS,　and　conduct　simulations　to　examine　its　finite　sample　performance.　The　results　of　simulation　study　indicate　that　the　QPC-SIS　performs　better　than　other　methods　like　sure　independent　screening　(SIS),　sure　independent　ranking　and　screening,　distance　correlation-sure　independent　screening,　conditional　correlation　sure　independence　screening　and　nonparametric　independent　screening,　which　shows　the　validity　and　rationality　of　QPC-SIS.