This study investigates the hydrometeor development and response to cloud droplet number concentration (CDNC)
perturbations in convection-permitting model configurations. We present results from a real-data simulation of deep convection
in the Congo basin, an idealised supercell case, and a warm-rain large-eddy simulation (LES). In each case we compare
two frequently used double-moment bulk microphysics schemes and investigate the response to CDNC perturbations. In the
Congo basin simulations both microphysics schemes have large positive biases in surface precipitation, frequency of high
radar reflectivities and frequency of cold cloud compared to observations. In all cases, differences in the simulated cloud
morphology and precipitation are found to be significantly greater between the microphysics schemes than due to CDNC
perturbations within each scheme. Further, we show that the response of the hydrometeors to CDNC perturbations strongly
differs not just between microphysics schemes but also between different cases of convection. Sensitivity tests show that the
representation of autoconversion is the dominant factor that drives differences in rain production between the microphysics
schemes in the idealised precipitating shallow cumulus case and in a sub-region of the Congo basin simulations dominated
by liquid-phase processes. In this region, rain mass is also shown to be relatively insensitive to the radiative effects of an
overlying layer of ice-phase cloud. In the idealised supercell case, thermodynamic impacts on the storm system using different
microphysics parameterisations can equal those due to aerosol effects. These results highlight the large uncertainty in cloud and
precipitation responses to aerosol in convection-permitting simulations and have important implications not just for modelling
studies of aerosol–convection interaction. These results indicate the continuing need for tighter observational constraints of
cloud processes and response to aerosol in a range of meteorological regimes.