In this study we assess the suitability of a recently introduced analog-based Model Output Statistics (MOS) downscaling method (referred to as MOS-Analog) for climate change studies and compare the results with a quantile mapping bias correction method. To this aim, we focus on Spain and consider daily precipitation output from an ensemble of Regional Climate Models provided by the ENSEMBLES project. The reanalysis-driven Regional Climate Model (RCM) data provide the historical data (with day-to-day correspondence with observations induced by the forcing boundary conditions) to conduct the analog search of the control (20C3M) and future (A1B) global climate model (GCM)-driven RCM values. First, we show that the MOS-Analog method outperforms the raw RCM output in the control 20C3M scenario (period 1971–2000) for all considered regions and precipitation indices, although for the worst-performing models the method is less effective. Second, we show that the MOS-Analog method broadly preserves the original RCM climate change signal for different future periods (2011–2040, 2041–2070, 2071–2100), except for those indices related to extreme precipitation. This could be explained by the limitation of the analog method to extrapolate unobserved precipitation records. These results suggest that the MOS-Analog is a spatially consistent alternative to standard bias correction methods, although the limitation for extreme values should be taken with caution in cases where this aspect is relevant for the problem.

This work was partially supported by the strategic action for energy and climate change by the Spanish R+D 2008–2011 Program ESTCENA (code 200800050084078), the project MULTI-SDM (CGL2015-66583- R, MINECO/FEDER), the Italian project of Interest NextData of the Italian Ministry for Education, University and Research, and by the European Science Foundation within the framework of COST ES1102 (Validating and integrating downscaling methods for climate change research). This paper has also been written under the framework of the International HYMEX project and the Spanish HOPE (CGL2014-52571-R) project. We also acknowledge the ENSEMBLES project (funded by the European Commission’s 6th Framework Programme through contract GOCE-CT-2003-505539) for the RCM data used in this work (http://ensemblesrt3.dmi.dk/). The authors thank AEMET and UC for the data provided for this work (Spain02 gridded precipitation data set, www.meteo.unican.es/es/datasets/spain02). Special thanks to the authors of the MeteoLab-Toolbox (www.meteo.unican.es/software/meteolab) which helped us to postprocess the data and to validate the method. Finally, we thank the anonymous referees for their useful comments.

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