We review the challenges and future perspectives of regional climate model (RCM), or dynamical downscaling, activities. Among the main technical issues in need of better understanding are those of selection and sensitivity to the model domain and resolution, techniques for providing lateral boundary conditions, and RCM internal variability. The added value (AV) obtained with the use of RCMs remains a central issue, which needs more rigorous and comprehensive analysis strategies. Within the context of regional climate projections, large ensembles of simulations are needed to better understand the models and characterize uncertainties. This has provided an impetus for the development of the Coordinated Regional Downscaling Experiment (CORDEX), the first international program offering a common protocol for downscaling experiments, and we discuss how CORDEX can address the key scientific challenges in downscaling research. Among the main future developments in RCM research, we highlight the development of coupled regional Earth system models and the transition to very high-resolution, cloud-resolving models.

We present an analysis of the added value (AV) of downscaling via regional climate model (RCM) nesting with respect to the driving global climate models (GCMs). We analyze ensembles of driving GCM and nested RCM (two resolutions, 0.44°and 0.11°) simulations for the late 20th and late 21st centuries from the CMIP5, EURO-CORDEX, and MED-CORDEX experiments, with a focus on the Alpine region. Different metrics of AV are investigated, measuring aspects of precipitation where substantial AV can be expected in mountainous terrains: spatial pattern of mean precipitation, daily precipitation intensity distribution, and daily precipitation extremes tails. Comparison with a high-quality, fine-scale (5 km) gridded observational data set shows substantial AV of RCM downscaling for all metrics selected, and results are mostly improved compared to the driving GCMs also when the RCM fields are upscaled at the scale of the GCM resolution. We also find consistent improvements in the high-resolution (0.11°) versus medium-resolution (0.44°) RCM simulations. Finally, we find that the RCM downscaling substantially modulates the GCM-produced precipitation change signal in future climate projections, particularly in terms of fine-scale spatial pattern associated with the complex topography of the region. Our results thus point to the important role that high-resolution nested RCMs can play in the study of climate change over areas characterized by complex topographical features.

We analyze ensembles (four realizations) of historical and future climate transient experiments carried out with the coupled atmosphere-ocean general circulation model (AOGCM) of the Hadley Centre for Climate Prediction and Research, version HADCM2, with four scenarios of greenhouse gas (GHG) and sulfate forcing. The analysis focuses on the regional scale, and in particular on 21 regions covering all land areas in the World (except Antarctica). We examine seasonally averaged surface air temperature and precipitation for the historical period of 1961±1990 and the future climate period of 2046±2075. Compared to previous AOGCM simulations, the HADCM2 model shows a good performance in reproducing observed regional averages of summer and winter temperature and precipitation. The model, however, does not reproduce well observed interannual variability. We ®nd that the uncertainty in regional climate change predictions associated with the spread of dierent realizations in an ensemble (i.e. the uncertainty related to the internal model variability) is relatively low for all scenarios and regions. In particular, this uncertainty is lower than the uncertainty due to inter-scenario variability and (by comparison with previous regional analyses of AOGCMs) with inter-model variability. The climate biases and sensitivities found for dierent realizations of the same ensemble were similar to the corresponding ensemble averages and the averages associated with individual realizations of the same ensemble did not dier from each other at the 5% con®dence level in the vast majority of cases. These results indicate that a relatively small number of realizations (3 or 4) is sucient to characterize an AOGCM transient climate change prediction at the regional scale.

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