LAUSR

The hourly summer precipitation simulations over East Asia by the Chinese Academy of Meteorological Science Climate System Model (CAMS-CSM) high-resolution Atmospheric Model Intercomparison Project (AMIP) runs (T255, ~50 km) were evaluated based on the merged hourly precipitation product released by the China Meteorological Administration (CMA). The results show that the simulation biases are closely related to the topography, with the precipitation amount and frequency overestimated (underestimated), and duration of precipitation events being longer (shorter), over the western high-altitude (eastern plain) regions of China. Six regions with large discrepancies were further analyzed. In terms of the frequency-intensity structure, the overestimation of precipitation frequency is mainly due to the excessive simulated weak precipitation over the four regions with positive biases: the southern edge of the Tibetan Plateau (STP), the northeastern edge of the Tibetan Plateau (NETP), the eastern periphery of the Tibetan Plateau (EPTP), and the mountainous area of North China (NCM); while the underestimation of frequency is mainly due to the insufficient precipitation with moderate intensity over the two regions with negative biases: lower reaches of the Yangtze River (LYR) and the South China coast (SCC). Based on the duration-diurnal structure analysis, two kinds of precipitation events with different natures can be distinguished. The long-duration night to early morning precipitation events have a significant contribution to the precipitation amount biases for all the six key regions, and this kind of precipitation mainly affects the precipitation diurnal variation over the mountainous areas or steep terrain. Although the short-duration afternoon precipitation events only have a greater contribution to the precipitation amount biases over the SCC region, this kind of precipitation affects the diurnal variation over the NCM region and the two key regions with negative biases. Such a detailed hourly-scale evaluation is helpful for enriching the understanding of simulation biases and to further improve model performance.