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The diurnal cycle of precipitation and precipitation variances at different time scales are analyzed in this study based on multiple high-resolution 3-h precipitation datasets. The results are used to evaluate nine CMIP6 models and a series of GFDL-AM4.0 model simulations, with the goal of examining the impact of SST diurnal cycle, varying horizontal resolutions, and different microphysics schemes on these two precipitation features. It is found that although diurnal amplitudes are reasonably simulated, models generally generate too early diurnal peaks over land, with a diurnal phase peaking around noon instead of the observed late afternoon (or early evening) peak. As for precipitation variances, irregular subdaily fluctuations dominate the total variance, followed by variance of daily mean precipitation and variance associated with the mean diurnal cycle. While the spatial and zonal distributions of precipitation variances are generally captured by the models, significant biases are present in tropical regions, where large mean precipitation biases are observed. The comparisons based on AM4.0 model simulations demonstrate that the inclusion of ocean coupling, adoption of a new microphysics scheme, and increasing of horizontal resolution have limited impacts on these two simulated features, emphasizing the need for future investigation into these model deficiencies at the process level. Conducting routine examinations of these metrics would be a crucial first step toward better simulation of precipitation intermittence in future model development. Last, distinct differences in these two features are found among observational datasets, highlighting the urgent need for a detailed evaluation of precipitation observations, especially at subdaily time scales, as model evaluation heavily relies on high-quality observations. Significance Statement: High-frequency precipitation data, such as 3-hourly or finer resolution, provide detailed and precise information about the intensity, timing, and location of individual precipitation events. This information is essential for evaluating physically based numerical weather and climate models, which are important tools for understanding and predicting precipitation changes. We compared several global high-resolution observation datasets with nine CMIP6 GCMs and a series of GFDL-AM4.0 model simulations to evaluate the precipitation diurnal cycle and variance, with the goal of examining the impact of SST diurnal cycle, varying horizontal resolutions, and different microphysics schemes on these metrics. Despite the impact of these factors on the simulated precipitation diurnal cycle and variance being evident, our results also show that they are not consistently aligned with observed features. This highlights the need for further investigation into model deficiencies at the process level. Therefore, conducting routine examinations of these metrics could be a crucial first step toward improving the simulation of precipitation intermittency in future model development. Additionally, given the large uncertainties, there is an urgent need for a detailed evaluation of observational precipitation products, particularly at subdaily time scales. [ABSTRACT FROM AUTHOR]

Accurate representation of mesoscale scale convective systems (MCSs) in climate models is of vital importance to understanding global energy, water cycles, and extreme weather. In this study, we evaluate the simulated MCS features over the United States from the newly developed GFDL global high-resolution (∼50 km) AM4 model by comparing them with the observations during spring to early summer (April–June) and late summer (July–August). The results show that the spatial distribution and seasonality of occurrence and genesis frequency of MCSs are reasonably simulated over the central United States in both seasons. The model reliably reproduces the observed features of MCS duration, translation speed, and size over the central United States, as well as the favorable large-scale circulation pattern associated with MCS development over the central United States during spring and early summer. However, the model misrepresents the amplitude and the phase of the diurnal cycle of MCSs during both seasons. In addition, the spatial distribution of occurrence and genesis frequency of MCSs over the eastern United States is substantially overestimated, with larger biases in early spring and summer. Furthermore, while large-scale circulation patterns are reasonably simulated in spring and early summer, they are misrepresented in the model during summer. Finally, we examine MCS-related precipitation, finding that the model overestimates MCS-related precipitation during spring and early summer, but this bias is insufficient to explain the significant dry bias observed in total precipitation over the central United States. Nonetheless, the dry biases in MCS-associated precipitation during late summer likely contribute to the overall precipitation deficit in the model. [ABSTRACT FROM AUTHOR]