Identifying a robust method to build RCMs ensemble as climate forcing for hydrological impact models

The regional climate models (RCMs) improve the understanding of the climate mechanism and are often used as climate forcing to hydrological impact models. Rainfall is the principal input to the water cycle, so special attention should be paid to its accurate estimation. However, climate change projections of rainfall events exhibit great divergence between RCMs. As a consequence, the rainfall projections, and the estimation of uncertainties, are better based in the combination of the information provided by an ensemble approach from different RCMs simulations. Taking into account the rainfall variability provided by different RCMs, the aims of this work are to evaluate the performance of two novel approaches based on the reliability ensemble averaging (REA) method for building RCMs ensembles of monthly precipitation over Spain. The proposed methodologies are based on probability density functions (PDFs) considering the variability of different levels of information, on the one hand of annual and seasonal rainfall, and on the other hand of monthly rainfall. The sensitivity of the proposed approaches, to two metrics for identifying the best ensemble building method, is evaluated. The plausible future scenario of rainfall for 2021-2050 over Spain, based on the more robust method, is identified. As a result, the rainfall projections are improved thus decreasing the uncertainties involved, to drive hydrological impacts models and therefore to reduce the cumulative errors in the modeling chain.