Geomagnetic storms pose critical risks to technological infrastructure, yet advance forecasting beyond 24 hours remains limited. Critical slowing down (CSD) — the tendency of complex systems to exhibit rising variance and autocorrelation as they approach bifurcation — provides a model-free early warning framework applicable to any system undergoing a critical transition. We apply CSD analysis to the daily planetary geomagnetic Ap index over 38 years (1987–2024; <em>n</em> = 13,880 days), computing a composite instability metric (rolling variance × |rolling first-order autocorrelation [AR(1)]|) across 30-, 60-, and 90-day windows. Receiver operating characteristic (ROC) analysis using DeLong standard errors demonstrates that the 30-day CSD index predicts major storms (Ap ≥ 30) with area under the curve (AUC) = 0.724 (95 % confidence interval [CI]: 0.705–0.744; Z-statistic = 22.49, <em>p</em> < 0.001) at a 30-day lead, significantly outperforming lagged Ap alone (ΔAUC = +0.132, <em>p</em> = 0.003). At the 90th-percentile CSD threshold, precision lift is 2.89× the base rate with Youden's J = 0.339. Pre-storm CSD elevation (−28.7 % above baseline) is in the expected direction but does not reach individual significance (Cohen's <em>d</em> = 0.22), while during-storm elevation is large (<em>d</em> = 1.36, <em>p</em> < 0.001). These results provide the first systematic demonstration that magnetospheric CSD dynamics, recoverable from a single surface index, contain actionable predictive information weeks before storm onset.