The assessment of local and regional impacts of climate change often requires downscaling of general circulation model (GCM) projections from coarser GCM-scale to finer localor catchmentscale spatial resolution. This paper provides an assessment of two downscaling approaches for simulation of daily rainfall over Sydney, Australia. The two downscaling alternatives compared include a multivariate...

To assess a species' vulnerability to climate change, we commonly use mapped environmental data that are coarsely resolved in time and space. Coarsely resolved temperature data are typically inaccurate at predicting temperatures in microhabitats used by an organism and may also exhibit spatial bias in topographically complex areas. One consequence of these inaccuracies is that coarsely resolved...

A key challenge in climate modeling is the assessment of the impact of global climate variables on regional weather measurements such as temperature and precipitation. This assessment is usually done by downscaling the output of (coarse resolution) global climate models to regional (high resolution) predictions. There are two independent downscaling pathways: dynamic and statistical. Dynamic do...

An improved statistical-dynamical downscaling method for the regionalization of large-scale climate analyses or simulations is introduced. The method is based on the disaggregation of a multi-year time-series of large-scale meteorological data into multi-day episodes of quasistationary circulation. The episodes are subsequently grouped into a de®ned number of classes. A regional model is used t...

An assessment is made of downscaling estimates of screen temperature and precipitation observed at 976 European stations during 1983–94. A statistical downscaling technique, in which local values are inferred from observed atmospheric predictor variables, is compared against two dynamical downscaling techniques, based on the use of the screen temperature or precipitation simulated at the neares...