[1] Precipitation downscaling improves the coarse resolution and poor representation of precipitation in global climate models and helps end users to assess the likely hydrological impacts of climate change. This paper integrates perspectives from meteorologists, climatologists, statisticians, and hydrologists to identify generic end user (in particular, impact modeler) needs and to discuss downscaling capabilities and gaps. End users need a reliable representation of precipitation intensities and temporal and spatial variability, as well as physical consistency, independent of region and season. In addition to presenting dynamical downscaling, we review perfect prognosis statistical downscaling, model output statistics, and weather generators, focusing on recent developments to improve the representation of spacetime variability. Furthermore, evaluation techniques to assess downscaling skill are presented. Downscaling adds considerable value to projections from global climate models. Remaining gaps are uncertainties arising from sparse data; representation of extreme summer precipitation, subdaily precipitation, and full precipitation fields on fine scales; capturing changes in small-scale processes and their feedback on large scales; and errors inherited from the driving global climate model.

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Six statistical and two dynamical downscaling models were compared with regard to their ability to downscale seven seasonal indices of heavy precipitation for two station networks in northwest and southeast England. The skill among the eight downscaling models was high for those indices and seasons that had greater spatial coherence. Generally, winter showed the highest downscaling skill and summer the lowest. The rainfall indices that were indicative of rainfall occurrence were better modelled than those indicative of intensity. Models based on non-linear artificial neural networks were found to be the best at modelling the inter-annual variability of the indices; however, their strong negative biases implied a tendency to underestimate extremes. A novel approach used in one of the neural network models to output the rainfall probability and the gamma distribution scale and shape parameters for each day meant that resampling methods could be used to circumvent the underestimation of extremes. Six of the models were applied to the Hadley Centre global circulation model HadAM3P forced by emissions according to two SRES scenarios. This revealed that the inter-model differences between the future changes in the downscaled precipitation indices were at least as large as the differences between the emission scenarios for a single model. This implies caution when interpreting the output from a single model or a single type of model (e.g. regional climate models) and the advantage of including as many different types of downscaling models, global models and emission scenarios as possible when developing climate-change projections at the local scale.

Investigation of the links between atmospheric circulation patterns and rainfall is important for the understanding of climatic variability and for the development of empirical circulation-based downscaling methods. Here, spatial and temporal variations in circulation-rainfall relationships over the Iberian Peninsula during the period 1958-97 are explored using an automated circulation classification scheme and daily rainfall totals for 18 stations. Links between the circulation classification scheme and the North Atlantic oscillation (NAO) are also considered, as are the direct links between rainfall and the NAO. Trends in rainfall and circulation-type frequency are explored. A general tendency towards decreasing mean seasonal rainfall over the peninsula, with the exception of the southeastern Mediterranean coast, hides larger changes in wet day amount and rainfall probability. There is a tendency towards more, less-intensive rain days across much of Iberia, with a tendency towards more, more-intensive rain days along the southeastern Mediterranean coast, both of which are reflected in changes in rainfall amount quantiles. A preliminary analysis indicates that these changes may have occurred systematically across all circulation types. Comparison of the trends in rainfall and in circulationtype frequency suggests possible links. These links are supported by linear regression analyses using circulation-type frequencies as predictor variables and rainfall totals for winter months as the predictands. The selected predictor variables reflect the main circulation features influencing winter rainfall across the peninsula, i.e. the strong influence of Atlantic westerly and southwesterly airmasses over much of the peninsula, of northerly and northwesterly surface flow over northern/northwestern Spain and northern Portugal and the stronger effect of Mediterranean rather than Atlantic influences in southeastern Spain. The observed rainfall changes cannot, however, be explained by changes in circulation alone.