Predicting　subsurface　temperatures　is　critical　for　comprehending　ocean　dynamics　and　climate　shifts.　This　study　presents　an　incremental　stable/dynamic　(SD)　disentanglement　learning　framework　merging　data-driven　methods　with　physics-based　insights.　It　separates　stable　and　dynamic　temperature　modes　to　untangle　intricate　spatiotemporal　interactions.　To　accommodate　ongoing　data　influx,　we　introduce　a　recursive　evolution　approach　for　updating　stable　representations,　employing　orthogonal-triangular　decomposition　(QR)　to　capture　incremental　information.　Moreover,　a　retrospective　learning　algorithm,　guided　by　temporal　changes　and　ocean　temperature　correlations,　is　employed　to　track　the　dynamic　behavior　adaptively.　The　subsurface　temperature　fields　can　be　efficiently　reconstructed　and　predicted　after　model　convergence.　Extensive　experiments　validate　the　model　across　various　depths　(-2.5　to　-800　m)　and　times　(from　May　1964　to　December　2021),　achieving　robust　performance　metrics:　root　mean　square　error　(RMSE)　of　0.1362,　mean　absolute　error　(MAE)　of　0.0901,　accuracy　(ACC)　of　0.9911,　and　coefficient　of　determination　(　R-2　)　between　predictions　and　observations　of　0.9998.　Comparative　analysis　underscores　the　proposed　method＇s　interpretability,　adaptability,　and　overall　performance　superiority.　Temperature　anomaly　analysis　accurately　identifies　subsurface　decadal　oscillations　in　the　low-　and　mid-latitude　Pacific　regions.