Abstract Accurate parameterizations of entrainment and detrainment rates (ε and δ) of shallow cumulus are crucial for improving simulations of atmospheric energy and water cycles. Existing ε and δ parameterizations are often derived from theoretical derivations, limited observations, or numerical simulations, which lack comprehensive global observational support. To address this limitation, parameterizations of shallow cumulus ε and δ are developed and evaluated using a global satellite‐derived data set. For parameterizations based on the physical approach, the recommended scheme incorporates environmental relative humidity (RHe) and vertical velocity to parameterize ε, while δ is parameterized using ε and RHe. Furthermore, the machine learning (ML) approach trained on thermodynamic, dynamic, and cloud microphysical properties can accurately predict ε and δ. Comparative analysis reveals that ML performs better than the physical approach. These findings provide valuable insights for refining cumulus parameterizations and enhancing the accuracy of climate model simulations.

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