The　time-varying　periodic　variations　in　Global　Navigation　Satellite　System　(GNSS)　stations　affect　the　reliable　time　series　analysis　and　appropriate　geophysical　interpretation.　In　this　study,　we　apply　the　singular　spectrum　analysis　(SSA)　method　to　characterize　and　interpret　the　periodic　patterns　of　GNSS　deformations　in　China　using　multiple　geodetic　datasets.　These　include　23-year　observations　from　the　Crustal　Movement　Observation　Network　of　China　(CMONOC),　displacements　inferred　from　the　Gravity　Recovery　and　Climate　Experiment　(GRACE),　and　loadings　derived　from　Geophysical　models　(GM).　The　results　reveal　that　all　CMONOC　time　series　exhibit　seasonal　signals　characterized　by　amplitude　and　phase　modulations,　and　the　SSA　method　outperforms　the　traditional　least　squares　fitting　(LSF)　method　in　extracting　and　interpreting　the　time-varying　seasonal　signals　from　the　original　time　series.　The　decrease　in　the　root　mean　square　(RMS)　correlates　well　with　the　annual　cycle　variance　estimated　by　the　SSA　method,　and　the　average　reduction　in　noise　amplitudes　is　nearly　twice　as　much　for　SSA　filtered　results　compared　with　those　from　the　LSF　method.　With　SSA　analysis,　the　time-varying　seasonal　signals　for　all　the　selected　stations　can　be　identified　in　the　reconstructed　components　corresponding　to　the　first　ten　eigenvalues.　Moreover,　both　RMS　reduction　and　correlation　analysis　imply　the　advantages　of　GRACE　solutions　in　explaining　the　GNSS　periodic　variations,　and　the　geophysical　effects　can　account　for　71%　of　the　GNSS　annual　amplitudes,　and　the　average　RMS　reduction　is　15%.　The　SSA　method　has　proved　to　be　useful　for　investigating　the　GNSS　time-varying　seasonal　signals.　It　could　be　applicable　as　an　auxiliary　tool　in　the　improvement　of　non-linear　variations　investigations.　©　2024　Editorial　office　of　Geodesy　and　Geodynamics