This study presents a comprehensive analysis of cloud condensation nuclei (CCN) phenomenology across nine observatories in diverse environments. We evaluate CCN prediction methods based on aerosol chemical composition and optical properties, including empirical and machine learning approaches. While simplified chemical schemes provide first-order estimates, incorporating optical data substantially improves CCN prediction accuracy in regions without direct measurements.