Regional climate modeling using convection‐permitting models (CPMs; horizontal grid spacing <4 km) emerges as a promising framework to provide more reliable climate information on regional to local scales compared to traditionally used large‐scale models (LSMs; horizontal grid spacing >10 km). CPMs no longer rely on convection parameterization schemes, which had been identified as a major source of errors and uncertainties in LSMs. Moreover, CPMs allow for a more accurate representation of surface and orography fields. The drawback of CPMs is the high demand on computational resources. For this reason, first CPM climate simulations only appeared a decade ago. In this study, we aim to provide a common basis for CPM climate simulations by giving a holistic review of the topic. The most important components in CPMs such as physical parameterizations and dynamical formulations are discussed critically. An overview of weaknesses and an outlook on required future developments is provided. Most importantly, this review presents the consolidated outcome of studies that addressed the added value of CPM climate simulations compared to LSMs. Improvements are evident mostly for climate statistics related to deep convection, mountainous regions, or extreme events. The climate change signals of CPM simulations suggest an increase in flash floods, changes in hail storm characteristics, and reductions in the snowpack over mountains. In conclusion, CPMs are a very promising tool for future climate research. However, coordinated modeling programs are crucially needed to advance parameterizations of unresolved physics and to assess the full potential of CPMs.

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Germany, Sweden, Norway, France, the Carpathians, and Spain. A clear result is that the 0.11 • simulations are found to better reproduce mean and extreme precipitation for almost all regions and seasons, even on the scale of the coarser-gridded simulations (50 km). This is primarily caused by the improved representation of orography in the 0.11 • simulations and therefore largest improvements can be found in regions with substantial orographic features. Improvements in reproducing precipitation in the summer season appear also due to the fact that in the fine-gridded simulations the larger scales of convection are captured by the resolved-scale dynamics. The 0.11 • simulations reduce biases in large areas of the investigated regions, have an improved representation of spatial precipitation patterns, and precipitation distributions are improved for daily and in particular for 3 hourly precipitation sums in Switzerland. When the evaluation is conducted on the fine (12.5 km) grid, the added value of the 0.11 • models becomes even more obvious.

In this study the added value of a ensemble of convection permitting climate simulations (CPCSs) compared to coarser gridded simulations is investigated. The ensemble consists of three non hydrostatic regional climate models providing five simulations with *10 and *3 km (CPCS) horizontal grid spacing each. The simulated temperature, precipitation, relative humidity, and global radiation fields are evaluated within two seasons (JJA 2007 and DJF 2007-2008 in the eastern part of the European Alps. Spatial variability, diurnal cycles, temporal correlations, and distributions with focus on extreme events are analyzed and specific methods (FSS and SAL) are used for indepth analysis of precipitation fields. The most important added value of CPCSs are found in the diurnal cycle improved timing of summer convective precipitation, the intensity of most extreme precipitation, and the size and shape of precipitation objects. These improvements are not caused by the higher resolved orography but by the explicit treatment of deep convection and the more realistic model dynamics. In contrary improvements in summer temperature fields can be fully attributed to the higher resolved orography. Generally, added value of CPCSs is predominantly found in summer, in complex terrain, on small spatial and temporal scales, and for high precipitation intensities.

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However, the MCS frequency is significantly underestimated in the central US during late summer. We discuss the origin of this frequency biases and suggest strategies for model improvements.