CMIP6 models，including the CAS-ESM, the earth-system model developed in CAS, China, tend to underestimate precipitation in southern and eastern China and overestimate precipitation in the east periphery of Tibetan Plateau. The underestimation and overestimation are especially prominent in the summer. These CMIP models, due to their coarse resolution (~100 km) , use conventional parameterizations based on experience-based triggering functions, causing large biases in simulating precipitation, particularly in warm seasons. On the other hand, the Multiscale Modeling Framework (MMF), which embeds a 2D cloud-resolving model in each column of the host global climate model, is a promising approach to address the deep convection problem by explicitly simulating the convection. Here, we therefore employ the MMF in the CAS-ESM and show that the MMF improves the precipitation simulation over East Asia.

We conduct two AMIP simulations, one with conventional convection parameterization and the other using the MMF. We then compare the simulated hourly and monthly precipitation with the precipitation from Global Precipitation Measurement Mission (GPM) and the Global Precipitation Climatology Project (GPCP). The results show that MMF-based model simulates summer precipitation in east Asia more realistically than the traditional CAS-ESM without MMF, alleviating the overestimation in the east periphery of Tibetan Plateau and reducing the dry bias occurred in southeastern China and west coastal Indo-China Peninsula in the model. Also, compared to the traditional CAS-ESM, MMF better simulates the annual cycle of precipitation in the eastern Tibet, southern and northeastern China, although the precipitation in southern China is overestimated to some degree. Moreover, the probability density function of hourly precipitation is improved as well in MMF, by producing more heavy rain.

extreme precipitation, multiscale modeling framework, global climate models