Clouds play an important role in the climate system’s global energy and water cycles. Representation of clouds, especially cumulus clouds, remains a great modeling challenge due to their variability in time and space, and the coarse grid-spacing in regional and global climate models. Even at the cloud-resolving scale, models with bulk (computationally inexpensive) microphysical formulas have difficulties simulating the convective (updraft) properties of cumulus clouds. Using the Weather Research and Forecasting (WRF) model, researchers at the Department of Energy’s Pacific Northwest National Laboratory found that compared to observations, the spectral-bin (more physical, more computationally expensive) microphysics method provides better simulations of precipitation and vertical velocity of the cumulus convective cores than two methods that use double-moment (mass and number) bulk microphysics. The spectral-bin microphysics method reproduces the observed updraft intensity well, alleviating much of the overestimation of updraft speeds produced by the bulk method. This suggests that a cloud microphysical method can improve model simulation of convective cloud properties. The researchers then used the spectral-bin microphysics model output as benchmark simulations for their study of scale-dependence of convection transport (Part II of the study). They also discovered that mass flux (rate of mass flow), a quantity on which cumulus representations are based, is very sensitive to different microphysical methods for tropical convection, indicating strong microphysics modification to convection. But, the modeled mass fluxes of cloud systems in the mid-latitudes are not sensitive to the choice of microphysics methods. Cloud microphysical measurements of rain, snow, and graupel in convective cores will be critically important to further understand and elucidate performances of cloud microphysics methods.