Global kilometer-scale models are the future of Earth system models as they can explicitly simulate organized convective storms and their associated extreme weather. Here, we comprehensively examined tropical mesoscale convective system (MCS) characteristics in the DYAMOND (DYnamics of the atmospheric general circulation modeled on non-hydrostatic domains) models for both summer and winter phases by applying eight different feature trackers to the simulations and satellite observations. Although different trackers produce substantial differences (a factor of 2-3) in observed MCS frequency and their contribution to total precipitation, model-observation differences in MCS statistics are more consistent among the trackers. DYAMOND models are generally skillful in simulating tropical mean MCS frequency, with multi-model mean biases of 2.9% over land and -0.5% over ocean. However, most models underestimate the MCS precipitation amount (23%) and their contribution to total precipitation (17%) relative to observations. These biases show large inter-model variability, but are generally smaller over land (13%) than over ocean (21%) on average. MCS diurnal cycle and cloud shield characteristics are better simulated than precipitation. Most models overestimate MCS precipitation intensity and underestimate stratiform rain contribution (up to a factor of 2), particularly over land. Models also predict a wide range of precipitable water in the tropics compared to reanalysis and satellite observations, and many models simulate a greater sensitivity of MCS precipitation intensity to precipitable water. The MCS metrics developed in this work provide process-oriented diagnostics for future model development efforts.

The arid region of Northwest China and Mongolia (NCM) receives most of the precipitation in the summer. The need for a better understanding of the synoptic-scale mechanism responsible for precipitation formation is accentuated by the recent wetting trend and its implications for future hydroclimate change. By conducting a hierarchical clustering analysis on an observationally-based daily precipitation dataset, we show that there are three distinct precipitation patterns over NCM, one of which is associated with strong precipitation over the western part of the region and another over the eastern part. The corresponding large-scale circulation anomalies indicate that these strong precipitation events are triggered by the upper-tropospheric disturbances in the form of transient Rossby wave packets. Furthermore, the wetting trend is linked to more frequent strong precipitation events over the eastern NCM, suggesting that it may have been induced remotely by atmospheric circulation perturbations.

Precipitation changes under global warming are widely studied. However, the direct radiative effect of CO2 on precipitation changes, independent from CO2-induced SST changes, has received less attention. Mechanistic understanding of the CO2 direct effect is therefore important and necessary. Utilizing multiple global atmospheric models, we identify robust summer precipitation changes across North America in response to the direct CO2 forcing. We find that spatial distribution of CO2 forcing at land surface is primarily shaped by climatological distribution of water vapor and clouds. This, coupled with changes in convection and moisture supply resulting from CO2-induced circulation changes, largely determines North America hydroclimate changes. In central North America, increasing CO2 decreases summertime precipitation by warming the surface and inducing dry advection into the region to reduce moisture supply. Meanwhile, for the southwest and the east, CO2-induced northward shift of subtropical highs generates wet advections to mitigate the drying effect from surface warming.

Changes in precipitation extremes remain a key uncertainty as the climate warms. Improved understanding of their evolution is crucial for effective water management. A number of studies have demonstrated various scaling relationships between precipitation extremes and several different environmental variables. In this chapter, we review recent important advances in two of these relationships primarily based on observations: The scaling of precipitation extremes with surface temperature (both air temperature and dew point temperature) and convective available potential energy (CAPE). Two up-to-date global daily datasets are also used to provide a further check on the generality of earlier findings. Known scaling relationships are used to quantify the impacts of these two factors on precipitation extremes. Results show that both of them play important roles, but their impacts vary over different regions on various time scales, highlighting the challenges of constructing global relationships to explain the changing nature of precipitation extremes.