This study evaluates convective cell properties and their relationships with convective and stratiform rainfall within a season-long convection-permitting simulation over central Argentina using measurements from the RELAMPAGO-CACTI field campaign. While the simulation reproduces the total observed rainfall, it underestimates stratiform rainfall by 46% and overestimates convective rainfall by 43%. As Convective Available Potential Energy (CAPE) increases, the overestimation of convective rainfall decreases, but the underestimation of stratiform rainfall increases such that the high bias in the contribution of convective rainfall to total rainfall remains approximately constant at 26% across all CAPE conditions. Overestimated convective rainfall arises from the simulation generating 2.6 times more convective cells than observed despite similar observed and simulated cell growth processes, with relatively wide cells contributing most to excessive convective rainfall. Relatively shallow cells, typically reaching heights of 4–7 km, contribute most to the cell number bias. This bias increases as CAPE decreases, potentially because cells and their updrafts become narrower and more under-resolved as CAPE decreases. The gross overproduction of shallow cells leads to overly efficient precipitation and inadequate detrainment of ice aloft, thereby diminishing the formation of robust stratiform rainfall regions. Decreasing the model’s horizontal grid spacing from 3 to 1 or 0.333 km for representative low and high CAPE cases results in minimal change to the cell number and depth biases, while the stratiform and convective rainfall biases also fail to improve. This suggests that improving prediction of deep convective system growth depends on factors beyond solely increasing model resolution.

To address the effect of stratiform latent heating on meso- to large scale circulations, an enhanced implementation of the Multiscale Coherent Structure Parameterization (MCSP) is developed for the Met Office Unified Model. MCSP represents the top-heavy stratiform latent heating from under-resolved organized convection in general circulation models. We couple the MCSP with a mass-flux convection scheme (CoMorph-A) to improve storm lifecycle continuity. The improved MCSP trigger is specifically designed for mixed-phase deep convective cloud, combined with a background vertical wind shear, both known to be crucial for stratiform development. We also test a cloud top temperature dependent convective-stratiform heating partitioning, in contrast to the earlier fixed partitioning. Assessments from ensemble weather forecasts and decadal simulations demonstrate that MCSP directly reduces cloud deepening and precipitation areas by moderating mesoscale circulations. Indirectly, it amends tropical precipitation biases, notably correcting dry and wet biases over India and the Indian Ocean, respectively. Remarkably, the scheme outperforms a climate model ensemble by improving seasonal precipitation cycle predictions in these regions. This enhancement is partly due to the scheme’s refinement of Madden-Julian Oscillation (MJO) spectra, achieving better alignment with reanalysis data by intensifying MJO events and maintaining their eastward propagation after passing the Maritime Continent. However, the scheme also increases precipitation overestimation over the Western Pacific. Shifting from fixed to temperature-dependent convective-stratiform partitioning reduces the Pacific precipitation overestimation but also lessens the improvements of seasonal cycle in India. Spatially correlated biases highlight the necessity for advancements beyond deterministic approaches to align MCSP with environmental conditions.