John R. Lanzante, Dennis Adams-Smith, Keith W. Dixon, Mary Jo Nath, and
Carolyn E. Whitlock:

Evaluation of Some Distributional Downscaling Methods as Applied to Daily
Maximum Temperature with Emphasis on Extremes.
Accepted by International Journal Of Climatology.

Abstract:

Statistical downscaling methods are extensively used to refine future climate
change projections produced by physical models. Distributional methods, which
are among the simplest to implement, are also among the most widely used,
either by themselves or in conjunction with more complex approaches. Here,
building off of earlier work we evaluate the performance of seven methods in
this class that range widely in their degree of complexity. We employ daily
maximum temperature over the Continental U. S. in a “Perfect Model” approach in
which the output from a large-scale dynamical model is used as a proxy for both
observations and model output. Importantly, this experimental design allows one
to estimate expected performance under a future high-emissions climate-change
scenario.
We examine skill over the full distribution as well in the tails, seasonal
variations in skill, and the ability to reproduce the climate change signal.
Viewed broadly, there generally are modest overall differences in performance
across the majority of the methods. However, the choice of philosophical
paradigms used to define the downscaling algorithms divides the seven methods
into two classes, of better vs. poorer overall performance. In particular, the
bias-correction plus change-factor approach performs better overall than the
bias-correction only approach. Finally, we examine the performance of some
special tail treatments that we introduced in earlier work which were based on
extensions of a widely used existing scheme. We find that our tail treatments
provide a further enhancement in downscaling extremes.