Today different modelling tools are available to provide climate change information at the regional scale. Coupled AOGCMs are the fundamental models used to simulate the climatic response to anthropogenic forcings and, to date, results from AOGCM simulations have provided the climate information for the vast majority of impact studies. On the other hand, resolution limitations pose severe constraints on the usefulness of AOGCM information, especially in regions characterised by complex physiographic settings. Three classes of regionalisation techniques have been developed to enhance the regional information of coupled AOGCMs: high resolution and variable resolution time-slice AGCM experiments, regional climate modelling, and empirical/statistical and statistical/dynamical approaches.

Since the SAR, substantial progress has been achieved in all regionalisation methods, including better understanding of the techniques, development of a wide variety of modelling systems and methods, application of the techniques to a wide range of studies and regional settings, and reduction of model biases. Modelling work has indicated that regionalisation techniques enhance some aspects of AOGCM regional information, such as the high resolution spatial detail of precipitation and temperature, and the statistics of daily precipitation events. It is important to stress that AOGCM information is the starting point for the application of all regionalisation techniques, so that a foremost requirement in the simulation of regional climate change is that the AOGCMs simulate well the circulation features that affect regional climates. In this respect, indications are that the performance of current AOGCMs is generally improving.

Analysis of AOGCM simulations for broad (sub-continental scale) regions indicates that biases in the simulation of present day regionally and seasonally averaged surface climate variables, although highly variable across regions and models, are generally improved compared with the previous generation models. This implies increased confidence in simulated climatic changes. The performance of models in reproducing observed interannual variability varies across regions and models.

Regional analysis of AOGCM transient simulations extending to 2100, for different scenarios of GHG increase and sulphate aerosol effects, and with a number of modelling systems (some simulations include ensembles of realisations) indicate that the average climatic changes for the late decades of the 21st century compared to present day climate vary substantially across regions and models. The primary source of uncertainty in the simulated changes is associated with inter-model range of changes, with inter-scenario and intra-ensemble range of simulated changes being less pronounced. Despite the range of inter-model results, some common patterns of sub-continental scale climatic changes are emerging, and thus providing increased confidence in the simulation of these changes.

Work performed with all regionalisation techniques indicates that sub-GCM grid scale structure in the regional climate change signal can occur in response to regional and local forcings, although more work is needed to assess the statistical significance of the sub-GCM grid scale signal. In particular, modelling evidence clearly indicates that topography, land use and the surface hydrologic cycle strongly affect the surface climate change signal at the regional to local scale. This implies that the use of AOGCM information for impact studies needs to be taken cautiously, especially in regions characterised by pronounced sub-GCM grid scale variability in forcings, and that suitable regionalisation techniques should be used to enhance the AOGCM results over these regions.

Considerations of various types may enter the choice of the regionalisation technique, as different techniques may be most suitable for different applications and different working environments. High resolution AGCMs offer the primary advantage of global coverage and two-way interactions between regional and global climate. However, due to their computational cost, the resolution increase that can be expected from these models is limited. Variable resolution and RCMs yield a greater increase in resolution, with current RCMs reaching resolutions as fine as a few tens of kilometres or less. RCMs can capture physical processes and feedbacks occurring at the regional scale, but they are affected by the errors of the AOGCM driving fields, and they do not represent regional-to-global climate feedbacks. The effects of regional-to-global feedback processes depend on the specific problem and in many cases may not be important. Two-way GCM-RCM nesting would allow the description of such effects, and some research efforts in that direction are currently under way. Statistical downscaling techniques offer the advantages of being computationally inexpensive, of providing local information which is needed in many impact applications, and of offering the possibility of being tailored to specific applications. However, these techniques have limitations inherent in their empirical nature.

The combined use of different techniques may provide the most suitable approach in many instances. For example, a high-resolution AGCM simulation could represent an important intermediate step between AOGCM information and RCM or statistical downscaling models. The convergence of results from different approaches applied to the same problem can increase the confidence in the results and differences between approaches can help to understand the behaviour of the models.

Despite recent improvements and developments, regionalisation research is still a maturing process and the related uncertainties are still rather poorly known. One of the reasons for this is that most regionalisation research activities have been carried out independently of each other and aimed at specific objectives. Therefore a coherent picture of regional climate change via available regionalisation techniques cannot yet be drawn. More co-ordinated efforts are thus necessary to improve the integrated hierarchy of models, evaluate the different methodologies, intercompare methods and models and apply these methods to climate change research in a comprehensive strategy.

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