Abstract
AbstractDownscaling coarse‐resolution model representations of climate is a disaggregation problem, in which any number of small‐scale weather sequences can be associated with a given set of large‐scale values. Because of this intrinsic indeterminacy it is natural and logically consistent for downscaling methods to include explicitly random elements. Weather generators are stochastic models for (usually) daily weather time series, which can be used for climate‐change downscaling through appropriate adjustments to their parameters. Two main approaches for such parametric adjustments have been developed, namely changes in the daily weather generator parameters based on imposed or assumed changes in the corresponding monthly statistics, and day‐by‐day changes to the generator parameters that are controlled by daily variations in simulated atmospheric circulation. This paper reviews and compares these two methods for weather‐generator‐based downscaling, focusing on the downscaling of precipitation. WIREs Clim Change 2010 1 898–907 DOI: 10.1002/wcc.85This article is categorized under: Assessing Impacts of Climate Change > Evaluating Future Impacts of Climate Change Assessing Impacts of Climate Change > Representing Uncertainty
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