Description and validation of a two-step analogue/regression downscaling method

• DOI: 10.1007/s00704-013-0836-x
• Publication Date: 2013-01-25T00:25:23Z
• Authors: J. Ribalaygua; L. Torres; J. Pórtoles; R. Monjo; E. Gaitán; M. R. Pino
• Keywords: Mean absolute error

This study describes a two-step analogue statistical downscaling method for daily temperature and precipitation. The first step is an analogue approach: the “n” days most similar to the day to be downscaled are selected. In the second step, a multiple regression analysis using the “n” most analogous days is performed for temperature, whereas for precipitation, the probability distribution of the “n” analogous days is used to define the amount of precipitation. Verification of this method has been carried out for the Spanish Iberian Peninsula and the Balearic Islands. Results show good performance for temperature (BIAS close to 0.1 °C and mean absolute errors around 1.9 °C) and an acceptable skill for precipitation (reasonably low BIAS except in autumn with a mean of −18 %, mean absolute error lower than for a reference simulation, i.e. persistence and a well-simulated probability distribution according to two non-parametric tests of similarity).