The accurate and timely estimation of earthquake magnitude is essential for effective tsunami early warning, particularly in seismically active regions such as Indonesia. Conventional seismic approaches are often hindered by magnitude saturation in significant events (Mw > 7.5), resulting in systematically underestimated magnitudes. To address this limitation, we develop a regional peak ground displacement (PGD) scaling law using high-rate GNSS (HR-GNSS) data from 21 moderate to large earthquakes in Indonesia. Based on 87 PGD observations, we construct a regression model that relates PGD, hypocentral distance, and moment magnitude (Mw). The PGD-derived magnitudes (MPGD) exhibit strong concordance with catalog moment magnitudes, achieving a mean absolute deviation (MAD) of 0.21 and surpassing the accuracy of previously published global models. Retrospective analyses reveal that MPGD estimates converge within 2–3 min for well-recorded events and remain robust, even for great and tsunamigenic earthquakes. These results underscore the potential of HR-GNSS data to complement conventional seismic networks, providing rapid and reliable magnitude estimates for operational tsunami early warning in Indonesia.

Accurate and timely earthquake magnitude estimation is critical for effective tsunami early warning, particularly in seismically active regions like Indonesia. Conventional seismic approaches often suffer from magnitude saturation for large events (Mw > 7.5), leading to underestimated magnitudes. To address this limitation, we propose a novel Peak Ground Displacement (PGD) Scaling Law based on regional high-rate GNSS (HR-GNSS) data from 21 moderate to large Indonesian earthquakes. Using 87 PGD measurements, we derive a regression model that relates PGD, hypocentral distance, and moment magnitude (Mw). The PGD-derived magnitudes (MPGD) show strong agreement with final moment magnitudes (Mw), achieving a mean absolute deviation (MAD) of 0.21—outperforming global models. Retrospective analyses demonstrate that the PGD estimates stabilize within 2–3 minutes for well-recorded events and remain robust even for great and tsunamigenic earthquakes. These findings confirm that HR-GNSS data can complement seismic networks, enabling rapid and reliable magnitude estimation in Indonesia.