Estimating　high-resolution　ocean　subsurface　temperature　has　great　importance　for　the　refined　study　of　ocean　climate　variability　and　change.　However,　the　insufficient　resolution　and　accuracy　of　subsurface　temperature　data　greatly　limits　our　comprehensive　understanding　of　mesoscale　and　other　fine-scale　ocean　processes.　In　this　study,　we　integrated　multiple　remote　sensing　data　and　in　situ　observations　to　compare　four　models　within　two　frameworks　(gradient　boosting　and　deep　learning).　The　optimal　model,　Deep　Forest,　was　selected　to　generate　a　high-resolution　subsurface　temperature　dataset　(DORS0.25　degrees)　for　the　upper　2000　m　from　1993　to　2023.　DORS0.25　degrees　exhibits　excellent　reconstruction　accuracy,　with　an　average　R-2　of　0.980　and　RMSE　of　0.579　degrees　C,　and　the　monthly　average　accuracy　is　higher　than　IAP　and　ORAS5　datasets.　Particularly,　DORS0.25　degrees　can　effectively　capture　detailed　ocean　warming　characteristics　in　complex　dynamic　regions　such　as　the　Gulf　Stream　and　the　Kuroshio　Extension,　facilitating　the　study　of　mesoscale　processes　and　warming　within　the　global-scale　ocean.　Moreover,　the　research　highlights　that　the　rate　of　warming　over　the　past　decade　has　been　significant,　and　ocean　warming　has　consistently　reached　new　highs　since　2019.　This　study　has　demonstrated　that　DORS0.25　degrees　is　a　crucial　dataset　for　understanding　and　monitoring　the　spatiotemporal　characteristics　and　processes　of　global　ocean　warming,　providing　valuable　data　support　for　the　sustainable　development　of　the　marine　environment　and　climate　change　actions.