In　the　northern　South　China　Sea　of　western　Pacific　Ocean　during　winter,　clouds,　sun　glint,　and　other　factors　block　optic　sensors,　leading　to　a　high　missing　rate　and　hence　a　major　concern　in　ocean　color　products　such　as　the　chlorophyll-a　(CHL)　data.　These　constraints　inhibit　the　understanding　of　CHL　variabilities　at　short　(seasonal)　scales.　Here,　we　introduce　a　new　gap-filling　method　to　reconstruct　data　gaps　in　a　daily　CHL　remote　sensing　product.　We　applied　discrete　cosine　transform　with　penalized　least　square　(DCT-PLS)　approach　in　the　adjacent　Luzon　Strait,　yielding　a　15-year　full-coverage　daily　4-km　CHL　product.　Against　the　cross-validation　set　and　an　independent　observational　data　set　collected　from　34　cruises,　evaluations　suggest　that　DCT-PLS　has　outperformed　the　widely　applied　classical　data-interpolating　empirical　orthogonal　function　(DINEOF)　method.　Besides,　the　DCT-PLS　method　is　characterized　by　more　efficient　computation.　The　complete　CHL　product　was　analyzed　with　a　particular　focus　on　the　intraseasonal　(30x2013;60　days)　control　on　the　winter　bloom　by　the　Madden-Julian　Oscillation　(MJO).　The　MJOx2019;s　local　signature　on　the　CHL　presents　asymmetry.　The　CHL　peaks　at　the　late　phases　of　MJO　events,　which　could　be　explained　by　the　relaxation　after　the　MJO-induced　wind　strengthening.　This　gap-filling　approach　can　be　promisingly　applied　in　other　remote　sensing　gap-filling　problems,　which　could　shed　light　on　the　short-term　variability　of　biological　and　physical　dynamics　in　the　ocean.