Independence　screening　is　a　variable　selection　method　that　uses　a　ranking　criterion　to　select　significant　variables,　particularly　for　statistical　models　with　nonpolynomial　dimensionality　or　＂large　p,　small　n＂　paradigms　when　p　can　be　as　large　as　an　exponential　of　the　sample　size　n.　In　this　paper　we　propose　a　robust　rank　correlation　screening　(RRCS)　method　to　deal　with　ultra-high　dimensional　data.　The　new　procedure　is　based　on　the　Kendall　tau　correlation　coefficient　between　response　and　predictor　variables　rather　than　the　Pearson　correlation　of　existing　methods.　The　new　method　has　four　desirable　features　compared　with　existing　independence　screening　methods.　First,　the　sure　independence　screening　property　can　hold　only　under　the　existence　of　a　second　order　moment　of　predictor　variables,　rather　than　exponential　tails　or　alikeness,　even　when　the　number　of　predictor　variables　grows　as　fast　as　exponentially　of　the　sample　size.　Second,　it　can　be　used　to　deal　with　semiparametric　models　such　as　transformation　regression　models　and　single-index　models　under　monotonic　constraint　to　the　link　function　without　involving　nonparametric　estimation　even　when　there　are　nonparametric　functions　in　the　models.　Third,　the　procedure　can　be　largely　used　against　outliers　and　influence　points　in　the　observations.　Last,　the　use　of　indicator　functions　in　rank　correlation　screening　greatly　simplifies　the　theoretical　derivation　due　to　the　boundedness　of　the　resulting　statistics,　compared　with　previous　studies　on　variable　screening.　Simulations　are　carried　out　for　comparisons　with　existing　methods　and　a　real　data　example　is　analyzed.