Abstract Foreshocks, though well‐documented phenomena preceding many large earthquakes, have limited forecasting utility due to their non‐pervasive occurrence and non‐distinctive characteristics. Using California as an example, we investigate how seismic monitoring capability, particularly the completeness magnitude (Mc ${M}{c}$), influences the inferred proportion of mainshocks with foreshocks (Pf ${P}{f}$). We test four foreshock identification methods, namely the fixed‐window, nearest neighbor clustering, empirical statistical (ES) methods and the epidemic‐type aftershock sequence (ETAS) model. The fixed‐window method shows Pf ${P}{f}$ decreasing with higher Mc ${M}{c}$ due to the misclassification of background events as foreshocks. In contrast, clustering and ES methods yield relatively stable Pf ${P}{f}$ across different Mc ${M}{c}$ values. The ETAS model suggests that many foreshocks in California are associated with aseismic driving processes, but the identification of the processes diminishes at high Mc ${M}{c}$. These results show that improved seismic monitoring capability does not significantly increase Pf ${P}{f}$ but is crucial for distinguishing processes driving foreshocks.

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