Evaluation of statistical downscaling methods for climate change projections over Spain: Present conditions with perfect predictors

Abstract

The Spanish Meteorological Agency (AEMET) is responsible for the elaboration of downscaled climate projections over Spain to feed the Second National Plan of Adaptation to Climate Change (PNACC-2). The main objective of this article is to establish a comparison among five statistical downscaling methods developed at AEMET: (1) Analog, (2) Regression, (3) Artificial Neural Networks, (4) Support Vector Machines and (5) Kernel Ridge Regression. This comparison has been carried out under present conditions and with perfect predictors, based on the framework established by the VALUE network, in particular, on its perfect predictor experiment. In this experiment, we evaluate the marginal aspects of the distributions of daily maximum/minimum temperatures and daily accumulated precipitation analysed by seasons, on a high resolution observational grid (0.05°) over mainland Spain and the Balearic Islands. This is the first of a set of three experiments aimed to allow us to decide which methods, and under what configuration, is more appropriate for the generation of downscaled climate projections over our region. For maximum/minimum temperatures, all methods display a similar behaviour. They capture very satisfactorily the mean values although slight biases are detected on the extremes. In general, results for maximum temperature appear to be more accurate than for minimum temperature, and the nonlinear methods display certain added value. For precipitation, remarkable differences are found among all methods. Most of the methods are capable of reproducing the total precipitation amount quite satisfactorily, whereas other aspects such as intense precipitations and the precipitation occurrence are captured with more accuracy by the Analog method.