Ultrahigh-dimensional variable screening plays an increasingly important part in diverse scientific areas and statistical researches.This paper mainly proposes two new feature screening approaches for varying coefficient mo-dels in ultrahigh-dimensional data analysis.One of them use the conditional quantile correlation corresponding to CQSIST as an utility measure of im-portance between the Tth quantile of response and predictor conditioning on the index variable,and the other utilizes the conditional distribution cor-relation,which corresponds to CQSIS,as an utility measure of significance between the response and predictor conditioning on the index variable.Un-der some regularization conditions,we establish the theoretical properties,including ranking consistency property and sure screening property.Simu-lation studies are conducted to evaluate the performance of the proposed methodologies.The simulations results show that our proposed approaches CQSIST and CQSIS significantly outperforms the existing methods in term of varying coefficient models.We also illustrate the performance of CQSIST and CQSIS through two real-data examples.Both theoretical and numerical studies demonstrate the effectiveness of the proposed methods.