Weather and climate models applied with sufficiently fine mesh grids to enable a large part of atmospheric deep convection to be explicitly resolved have shown a significantly improved representation of local, short-duration and intense precipitation events compared to coarser scale models. In this thesis, two studies are presented aimed at exploring the dependence of horizontal resolution and of parameterization of convection on the simulation of precipitation. The first examined the ability of HARMONIE Climate (HCLIM) regional climate model to reproduce the recent climate in Europe with two different horizontal resolutions, 15 and 6.25 km. The latter is part of the ”grey-zone” resolution interval corresponding to approximately 3-10 km. Particular focus has been given to rainfall and its spatial and temporal variability and other characteristics, for example intensity distributions. The model configuration with the higher resolution is much better at simulating days of large accumulated precipitation amounts, most evident when the comparison is made against high-resolution observations. Otherwise, the two simulations show similar skill, including the representation of the spatial structure of individual rainfall areas of primarily convective origin. The results suggest a ”scale-awareness” in HCLIM, which supports a central feature of the model’s description of deep convection as it is designed to operate independently of the horizontal resolution. In the second study, summer season precipitation over the Alps region, as simulated by HCLIM at different resolutions, is investigated. Similar model configurations as in the previous study were used, but in addition a simulation at the ”convection-permitting” 2 km resolution has been made over Central Europe. The latter considerably increases the realism compared to the former regarding the distribution and intensities of precipitation, as well as other important characteristics including the duration of rain spells, particularly on sub-daily time scales and for extreme events. The simulations with cumulus parameterization active underestimate short-duration heavy rainfall, and rainspells with low peak intensities are too persistent. Furthermore, even though the 6.25 km simulation generally reduces the biases seen in the 15 km run, definitive conclusions of the benefit of ”grey-zone” resolution is difficult to establish in context of the increased requirement of computer resources for the higher-resolution simulation.

This work is motivated by the question: how much complexity is appropriate for a cloud parameterization used in general circulation models (GCM). To approach this question, cloud parameterizations across the complexity range are explored using general circulation models and theoretical Monte-Carlo simulations. Their results are compared with high-resolution satellite observations and simulations that resolve the GCM subgrid-scale variability explicitly. A process-orientated evaluation is facilitated by GCM forecast simulations which reproduce the synoptic state. For this purpose novel methods were develop to a) conceptually relate the underlying saturation deficit probability density function (PDF) with its saturated cloudy part, b) analytically compute the vertical integrated liquid water path (LWP) variability, c) diagnose the relevant PDF-moments from cloud parameterizations, d) derive high-resolution LWP from satellite observations and e) deduce the LWP statistics by aggregating the LWP onto boxes equivalent to the GCM grid size. On this basis, this work shows that it is possible to evaluate the sub-grid scale variability of cloud parameterizations in terms of cloud variables. Differences among the PDF types increase with complexity, in particular the more advanced cloud parameterizations can make use of their double Gaussian PDF in conditions, where cumulus convection forms a separate mode with respect to the remainder of the grid-box. Therefore, it is concluded that the difference between unimodal and bimodal PDFs is more important, than the shape within each mode. However, the simulations and their evaluation reveals that the advanced parameterizations do not take full advantage of their abilities and their statistical relationships are broadly similar to less complex PDF shapes, while the results from observations and cloud resolving simulations indicate even more complex distributions. Therefore, this work suggests that the use of less complex PDF shapes might yield a better trade-off. With increasing model resolution initial weaknesses of simpler, e.g. unimodal PDFs, will be diminished. While cloud schemes for coarse-resolved models need to parameterize multiple cloud regimes per grid-box, higher spatial resolution of future GCMs will separate them better, so that the unimodal approximation improves.