Offshore　structures,　such　as　oil　and　gas　platforms　and　offshore　wind　turbines,　are　subjected　to　wind　and　wave　loads　simultaneously　during　their　service　lifetime.　Since　the　wind　and　wave　states　are　of　significant　randomness　and　dependence,　the　probabilistic　modeling　of　joint　wind　and　wave　conditions　plays　an　essential　role　in　the　safety　design　of　offshore　structures.　Currently,　three　different　methods　can　be　adopted　to　establish　the　joint　probabilistic　model,　which,　however,　are　somewhat　inconvenient　in　applications.　The　recently　emerged　generative　adversarial　networks　has　been　demonstrated　to　be　effective　in　dealing　with　high-dimensional　random　variables　in　several　fields.　In　this　study,　the　implicit　joint　probabilistic　model　of　joint　wind　and　wave　load　conditions　is　developed　based　on　the　Wasserstein　generative　adversarial　network　with　gradient　penalty.　Long-term　metocean　reanalysis　data　of　the　site　in　the　South　China　Sea　is　used　to　train　and　validate　the　model.　After　one　million　training　steps,　high-quality　samples　that　are　quite　similar　to　the　original　data　can　be　generated　by　the　developed　model.　In　addition,　statistical　comparisons　of　the　generated　samples　obtained　by　the　C-vine　copula　approach　and　the　developed　generative　adversarial　network　model　are　performed　as　well,　which　demonstrates　the　effectiveness　and　superiority　of　the　developed　model.