Recent observational and theoretical studies show a systematic relationship between tropical moist convection and measures related to large-scale convergence. It has been suggested that cloud fields in the column stochastic multicloud model compare better with observations when using predictors related to convergence rather than moist energetics (e.g., CAPE) as per Peters et al. Here, this work is extended to a fully prognostic multicloud model. A nonlocal convergence-coupled formulation of the stochastic multicloud model is implemented without wind-dependent surface heat fluxes. In a series of idealized Walker cell simulations, this convergence coupling enhances the persistence of Kelvin wave analogs in dry regions of the domain while leaving the dynamics in moist regions largely unaltered. This effect is robust for changes in the amplitude of the imposed sea surface temperature (SST) gradient. In essence, this method provides a soft convergence coupling that allows for increased interaction between cumulus convection and the large-scale circulation but does not suffer from the deleterious wave–conditional instability of the second kind (CISK) behavior of the Kuo-type moisture-convergence closures.