This　study　aims　to　provide　valuable　scientific　insights　into　various　estimation　techniques　of　geocentre　motion　(GCM)　from　the　perspective　of　signal　analysis,　thereby　enhancing　Gravity　Recovery　and　Climate　Experiment　(GRACE)　users＇　understanding　and　application　of　GCM.　Initially,　it　utilizes　the　satellite　laser　ranging　(SLR)　technique　with　the　network　shift　approach　to　estimate　over　30　yr　of　weekly　GCM　time-series　from　1994　to　2024.　Subsequently,　we　employ　two　approaches　to　estimate　three　types　of　monthly　GCM　time-series　spanning　more　than　20　yr　from　2002　to　2023:　combining　GRACE　data　with　an　ocean　bottom　pressure　model　(GRACE-OBP　approach),　the　fingerprint　approach　(FPA),　and　the　fingerprint　approach　with　satellite　altimetry　data　(FPA-SA,　up　to　2022).　The　former　is　referred　to　as　SLR-based　GCM　estimates,　while　the　latter,　which　uses　GRACE　Earth＇s　gravity　field　models,　is　termed　GRACE-based　GCM　estimates.　Furthermore,　this　study　pioneers　the　use　of　multichannel　singular　spectrum　analysis　(MSSA)　for　GCM　analysis,　especially　focusing　on　the　latest　GRACE-based　GCM　estimates　from　the　GRACE-OBP　and　FPA/FPA-SA　approaches,　marking　the　first　comprehensive　analysis　of　GCM　estimated　by　various　techniques.　The　results　show　that　MSSA　can　effectively　extract　common　signals　from　the　three　components　of　the　GCM　time-series.　The　seasonal　components　extracted　from　GRACE-based　GCM　estimates　using　MSSA　are　consistent　with　those　from　SLR-based　GCM　estimates,　although　the　former　exhibit　slightly　larger　amplitudes　of　the　annual　and　semi-annual　signals.　After　correcting　the　atmosphere-ocean　dealiasing,　the　amplitudes　of　the　SLR-based　estimates　correspondingly　decrease,　remaining　slightly　larger　but　becoming　closer　to　those　of　the　GRACE-based　estimates.　However,　a　periodic　signal　with　an　approximate　160-d　period　is　detectable　in　all　GRACE-based　GCM　estimates,　but　it　is　absent　in　SLR-based　GCM　estimates.　Further　investigation　using　MSSA　into　higher　degree　spherical　harmonic　(SH)　coefficients　of　the　Earth＇s　gravity　field　models　reveals　that　these　SH　coefficients　contain　a　160-d　periodic　signal.　This　finding　suggests　that　the　signal　detected　in　GRACE-based　GCM　estimates　originates　from　systematic　errors　in　these　SH　coefficients,　offering　new　insights　for　improving　the　accuracy　of　GRACE　Earth＇s　gravity　field　solutions.　Additionally,　GRACE-based　GCM　estimates　show　significant　secular　non-zero　trends,　notably　larger　than　those　in　SLR-based　GCM　estimates,　which　are　not　expected　to　exhibit　any　trends.　However,　the　reliance　of　GRACE-based　GCM　estimates　on　geophysical　models　(e.g.　glacier　melting,　glacial　isostatic　adjustment　and　hydrological　models)　limits　the　accuracy　of　their　trends,　underscoring　the　need　for　further　validation.　Overall,　this　study　highlights　new　challenges　regarding　the　accuracy　of　GRACE-based　GCM　estimates　and　emphasizes　the　necessity　for　further　validation　in　mass　change　research.