The lack of precursory signals at some volcanic eruptions could be due to the analysis performed which failed to capture subtle changes. We have developed a new technique, “Subtle Precursor Measurement of Change in Frequency (SuPreMeChiF)”, which calculates the cumulative distribution difference (Kolmogorov-Smirnov test) between monitoring features in given reference and sample windows, to detect and quantify subtle changes in continuous data that may be overlooked by usual analysis. It is tested on seismic and infrasound recordings to analyse changes associated with the COVID-19 period, a known global perturbation. The results show high coherence with mobility, reveal details of changes that were not indicated in conventional spectral analysis, and demonstrate the potential to retrieve the source physical processes. This quantitative approach provides insight for future application in automated detections during real-time volcano monitoring.