Since the SAR, significant improvements have been achieved in the areas of development and understanding of the nested regional climate modelling technique. These include many new RCM systems, multiple nesting, coupling with different components of the climate system and research into the effects of domain size, resolution, boundary forcing and internal model variability. As a result, a number of RCM systems are currently available with the capability of high-resolution, multi-decadal simulations in a variety of regional settings. Nested RCMs have shown marked improvements in their ability to reproduce present day average climate, with some of this improvement due to better quality driving fields provided by GCMs. Seasonal temperature and precipitation biases in state-of-the-art RCMs are generally less than 1 to 2°C and a few percent to 50 to 60 % of observed precipitation, respectively, over regions of size 10⁵ to 10⁶ km². However, it is imperative for the effective use of RCMs in climate change work that the quality of GCM large-scale driving fields continues to improve. Research aiming at reducing systematic errors in both GCMs and RCMs should be carried out. With significantly improved model systems the evidence, so far, indicates that improved regional climate change simulations can be produced in the near future.

The analysis of RCM simulations has extended beyond simple averages to include higher-order climate statistics, and has indicated that RCMs can effectively reproduce interannual variability when driven by good quality forcing fields. However, more anlysis and improvements are needed of the model performance in simulating climate variability at short time-scales (daily to sub-daily).

A serious problem concerning RCM evaluation is a general lack of good quality high-resolution observed data. In many areas, observations are extremely sparse due to complex geography or remoteness of settings. In addition, only a little work has been carried out on how to use point measurements to evaluate the grid-box mean values from a climate model, especially when using sparse station networks. This limits the ability to assess model skill in complex terrain and remote regions. It is essential for the advancement of regional climate understanding and modelling, that more research aiming at improving the quality of data for model evaluation is performed.

Overall, the evidence is strong that regional models consistently improve the spatial detail of simulated climate compared to GCMs because of their better representation of sub-GCM grid scale forcings, especially in regard to the surface hydrologic budget. This is not necessarily the case for region-averaged climate. The increased resolution of RCMs also allows the simulation of a broader spectrum of weather events, in particular concerning higher order climate statistics such as daily precipitation intensity distributions. Analysis of some RCM experiments indicate that this is in the direction of increased agreement with observations.

Several RCM studies have been important for understanding climate change processes, such as the elevation signature of the climate change signal or the effect of climate change at the river catchment level. However, a consistent set of RCM simulations of climate change for different regions which can be used as climate change scenarios for impact work is still not available. Most RCM climate change simulations have been sensitivity and process studies aimed at specific goals. The need is there to co-ordinate RCM simulation efforts and to extend studies to more regions so that ensemble simulations with different models and scenarios can be developed to provide useful information for impact assessments. This will need to be achieved under the auspices of international or large national programmes. Within this context, an important issue is to provide RCM simulations of increasing length to minimise limitations due to sampling problems.

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