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 local- or catchment-scale 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 multisite statistical downscaling model based on semi-parametric conditional simulation and a dynamical downscaling approach that uses the National Center for Atmospheric Research (NCAR) weather research and forecasting (WRF) model. The two approaches are evaluated for their ability to reproduce important at-site rainfall statistics at a network of 45 raingauge stations and regional statistics over the catchment area of the Warragamba Dam (9,050 km2). The results indicate that the simulations from these approaches capture many regionally observed climate features, including the simulated seasonal and annual means and daily extreme rainfall values. Further analyses suggest that the statistical downscaling approach provides improved simulations of attributes related to point rainfall, spell lengths and amounts, whereas the dynamical approach is well-suited for applications where regionally averaged rainfall is of primary concern.

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