For　a　conditional　density　function　(CDF)　in　the　right-censored　model,　the　local　linear　(LL)　estimator　has　superior　bias　properties　compared　with　the　Nadaraya-Watson　(NW)　one,　but　it　may　have　negative　values　and　thus　give　rise　to　unsuitable　inference.　In　order　to　alleviate　the　possible　negativity　of　the　LL　estimator,　we　define　a　reweighted　NW　(RNW)　estimator　of　the　CDF　in　the　right-censored　model　by　employing　the　empirical　likelihood　(EL)　method.　The　RNW　estimator　is　constructed　by　modifying　the　NW　estimator　slightly,　so　it　naturally　inherits　the　nonnegativity　of　the　NW　one.　It　is　assumed　that　the　censoring　time　is　independent　of　the　survival　time　with　the　associated　covariate.　Under　stationary　alpha-mixing　observations,　the　weak　consistency　and　asymptotic　normality　of　the　RNW　estimator　are　developed.　The　asymptotic　normality　shows　that　the　RNW　estimator　possesses　the　bias　and　variance　of　the　LL　estimator.　Finally,　we　conduct　simulations　to　evaluate　the　finite　sample　performance　of　the　estimator.　(C)　2020　Elsevier　B.V.　All　rights　reserved.