A　probabilistic　approach　is　developed　to　estimate　multivariate　extreme　wind　loads　by　introducing　copula　theory.　Three　major　concerns　are　addressed:　(1)　the　characterization　of　pairwise　dependence　among　extreme　load　coefficients,　(2)　the　construction　of　a　probability　model　of　multivariate　extreme　load　coefficients,　and　(3)　the　probabilistic　estimation　of　multivariate　extreme　wind　loads　with　randomness　of　the　mean　wind　speed　(i.e.,　wind　climate　change).　Theoretical　and　numerical　analyses　are　carried　out　with　the　aid　of　wind　tunnel　data.　The　results　show　that　using　rank　dependence　(Kendall＇s　tau　and　Spearman＇s　rho)　is　more　appropriate　than　using　Pearson　correlation　coefficient　in　defining　dependence　for　extreme　load　coefficients.　The　Gaussian　copula　is　convenient　for　deriving　the　joint　distribution　of　multivariate　extreme　load　coefficients　but　is　not　applicable　for　high-dimensional　problems.　In　contrast,　the　vine　copula　is　flexible　and　can　provide　a　better　estimate　of　the　joint　distribution　function　without　dimension　limitations.　Multivariate　annual　maximum　wind　loads　can　be　estimated　via　either　first-　or　full-order　methods.　Dependence　of　the　extreme　load　coefficients　and　randomness　of　the　wind　speed　are　both　found　having　effects　on　the　dependence　of　extreme　wind　loads.　Moreover,　the　procedure　of　simulating　multivariate　annual　maximum　wind　loads　is　presented　to　facilitate　the　use　in　practical　problems.