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Abstract

Cloud microphysics plays an important role in modulating precipitation efficiency ε_p, the percentage of atmospheric condensate that reaches the surface as precipitation. Precipitation intensity can be scaled as the product of this efficiency and an integrated condensation rate. With the availability of storm-resolving models (SRMs), atmospheric ascent rates and hence integrated condensation rates are more reliably simulated, meaning that constraint of precipitation efficiencies is becoming increasingly important. Precipitation efficiency has been quantified at coarse resolutions from the CMIP6 and RCEMIP ensembles, as well as MODIS and TRMM data, but not yet from full-complexity SRMs. In this study, the output of three models from the Dynamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains (DYAMOND) initiative (GEOS-5, ICON, and FV3) are used to quantify the inter-model variability in ε_p, as well as its dependence on the degree of convective organization as encapsulated in the area-based convective organization potential metric of Jin et al. (2022). We calculate ε_p, over South Asia during the monsoon with the DYAMOND Summer data, using the ratio of surface convective precipitation to both the integrated condensation rate and the column-integrated cloud water content (Li et al. 2022). The latter technique enables comparison with satellite observations, and we use this feature in evaluations of the simulated ε_p against those calculated from MODIS cloud water path and TRMM rainfall data over the same region. We begin the investigation of why ε_p varies between models and degrees of convective organization by examining cloud condensate partitioning and sizing.