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An increasing number of studies comparing different downscaling studies have emerged since the SAR. However, there is a paucity of systematic studies that use common data sets applied to different procedures and over the same geographic region. A number of articles discussing different empirical and dynamical downscaling approaches present summaries of the relative merits and shortcomings of different procedures (Giorgi and Mearns, 1991; Hewitson and Crane, 1996; Rummukainen, 1997; Wilby and Wigley, 1997; Gyalistras et al., 1998; Kidson and Thompson, 1998; Biau et al., 1999; Murphy, 1999; ; von Storch, 1999b; Zorita and von Storch, 1999; Murphy, 2000). However, these inter-comparisons vary widely with respect to predictors, predictands and measures of skill. Consequently, a systematic, internationally co-ordinated inter-comparison project would be particularly helpful in addressing this issue.
The most systematic and comprehensive study so far compared empirical transfer functions, weather generators, and circulation classification schemes over the same geographical region using climate change simulations and observational data (Wilby and Wigley, 1997; Wilby, 1998). This considered a demanding task to downscale daily precipitation for six locations over North America, spanning arid, moist tropical, maritime, mid-latitude, and continental climate regimes. Fourteen measures of skill were used, strongly emphasising daily statistics, and included wet and dry spell length, 95th percentile values, wet-wet day probabilities, and several measures of standard deviation. Downscaling procedures in the study included two different weather generators, two variants of an ANN-based technique, and two stochastic/circulation classification schemes based on vorticity classes.
The results require careful evaluation as they indicate relative merits and shortcoming of the different procedures rather than recommending one over another. Overall, the weather generators captured the wet-day occurrence and the amount distributions in the data well, but were less successful at capturing the interannual variability, while the opposite results was found for the ANN procedures. The stochastic/circulation typing schemes, as something of a combination of the principles underlying the other methods, were a better all-round performer.
A factor not yet fully evaluated in any comparative study is that of the temporal evolution of daily events which may be critical for some applications, e.g., hydrological modelling. While a downscaling procedure may correctly represent, for example, the number of rain days, the temporal sequencing of these may be as important. Zorita et al. (1995) and Zorita and von Storch (1997) compared a CART technique, a CCA and an ANN technique with the analogue technique, and found the simpler analogue technique performed as well as the more complicated methods.
A number of analyses have dealt with the relative merits of non-linear and linear approaches. For example, the relationships between daily precipitation and circulation indicators are often non-linear (Conway et al., 1996; Brandsma and Buishand, 1997). Similarly, Corte-Real et al. (1995) applied multivariate adaptive regression splines (MARS) to approximate the non-linear relationships between large-scale circulation and monthly mean precipitation. In a comparison of kriging and analogues, Biau et al. (1999) and von Storch (1999c) show that kriging resulted in better specifications of averaged quantities but too low variance, whereas analogues returned the right variance but lower correlation. In general, it appears that downscaling of the short-term climate variance benefits from the use of non-linear models.
Most of the comparative studies mentioned above come to the conclusion that techniques differ in their success of specifying regional climate, and the relative merits and shortcomings emerge differently in different studies and regions. This is not surprising, as there is considerable flexibility in setting up a downscaling procedure, and the suitability of a technique and the adaptation to the problem at hand varies. This flexibility is a distinct advantage of empirical methods.
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