Recent interest in geophony-based approaches for rainfall monitoring has highlighted its potential as a low-cost complement to traditional methods. Yet, most existing techniques rely on model training and lack cross-site validation. In this study, we introduce and evaluate a simple acoustic metric for rainfall characterization based on deviations in power spectral density from a local baseline under dry-weather conditions. The method was applied to eight datasets containing audio recordings from tropical and temperate forests, as well as urban and semi-urban environments, and validated using rain gauge measurements. Results show consistently high correlations (≈ 0.7) between the proposed metric and rainfall rate for the tropical (Amazonian and West African) datasets when low-frequency bands are chosen (0.2–0.5 kHz), as opposed to the<br />open-air dataset, which did not present significant correlation values. Additionally, for the temperate forest dataset (France), higher correlations are obtained on the higher part of the spectra (3.2–3.5 kHz), likely due to wind and technophonic-related interferences (jet engine background noise) at lower frequencies. Although common frequency bands are identified across multiple sites, the amplitude of the proposed metric varies substantially between regions, indicating that a universal physical relationship for direct rainfall rate estimation may be difficult to achieve. These results highlight both the potential and the limitations of a simple, training-free acoustic metric for rainfall monitoring across diverse environments.