Exploring CME–Sunspot Correlations Through Multi-Variable Statistical Analysis of CME Parameters

Abstract Coronal mass ejections (CMEs) are among the most energetic solar phenomena, and their link to sunspot activity—the standard proxy of magnetic variability—is key for forecasting space weather. Previous studies reported correlations but relied mainly on coarse averages and heterogeneous populations. Here, we present a multivariate statistical analysis of CME occurrence rates and sunspot numbers (SSN) across Solar Cycles 23–25 (1996–2024). Our methodology incorporates systematic population stratification by angular width, dynamic velocity thresholds, and bootstrap confidence intervals to ensure statistical robustness. Two distinct regimes emerge. At the annual scale, the strongest correlations occur at velocities above the mean CME speed (scaling factors 1.17–1.34), with values up to r ≈ 0.97. At the monthly scale, however, the pattern reverses: slower CMEs exhibit the closest correspondence with SSN. Solar Cycle 24, in particular, displays exceptionally high correlations at very low velocity ranges. Extending this approach to Solar Cycle 25 (2020–2024), we find that even during its ascending and early-maximum phases, correlations remain strong (r > 0.9 at monthly resolution). These findings demonstrate that CME–sunspot coupling depends both on temporal resolution and on CME subpopulation. They further establish a physically grounded framework for translating sunspot based solar cycle predictions into targeted CME occurrence forecasts, thereby enhancing the predictive toolkit for space weather.