Enabling　precise　forecasting　of　the　remaining　useful　life　(RUL)　for　machines　can　reduce　maintenance　cost,　increase　availability,　and　prevent　catastrophic　consequences.　Data-driven　RUL　prediction　methods　have　already　achieved　acclaimed　performance.　However,　they　usually　assume　that　the　training　and　testing　data　are　collected　from　the　same　condition　(same　distribution　or　domain),　which　is　generally　not　valid　in　real　industry.　Conventional　approaches　to　address　domain　shift　problems　attempt　to　derive　domain-invariant　features,　but　fail　to　consider　target-specific　information,　leading　to　limited　performance.　To　tackle　this　issue,　in　this　article,　we　propose　a　contrastive　adversarial　domain　adaptation　(CADA)　method　for　cross-domain　RUL　prediction.　The　proposed　CADA　approach　is　built　upon　an　adversarial　domain　adaptation　architecture　with　a　contrastive　loss,　such　that　it　is　able　to　take　target-specific　information　into　consideration　when　learning　domain-invariant　features.　To　validate　the　superiority　of　the　proposed　approach,　comprehensive　experiments　have　been　conducted　to　predict　the　RULs　of　aeroengines　across　12　cross-domain　scenarios.　The　experimental　results　show　that　the　proposed　method　significantly　outperforms　state-of-the-arts　with　over　21%　and　38%　improvements　in　terms　of　two　different　evaluation　metrics.