Screening CMIP6 models for Chile based on past performance and code genealogy

We describe and demonstrate a two-step approach for screening global climate models (GCMs) and produce robust annual and seasonal climate projections for Chile. First, we assess climate model simulations through a Past Performance Index (PPI) inspired by the Kling-Gupta Efficiency, which accounts for climatological averages, interannual variability, seasonal cycles, monthly probabilistic distribution, spatial patterns of climatological means, and the capability of the GCMs to reproduce teleconnection responses to El Niño Southern Oscillation (ENSO) and the Southern Annular Mode (SAM). The PPI formulation is flexible enough to include additional variables and evaluation metrics and weight them differently. Secondly, we use a recently proposed GCM classification based on model code genealogy to obtain a subset of independent model structures from the top 60% GCMs in terms of PPI values. We use this approach to evaluate 27 models from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) and generate projections in five regions with very different climates across continental Chile. The results show that the GCM evaluation framework is able to identify pools of poor-performing and well-behaved models at each macrozone. Because of its flexibility, the model features that may be improved through bias correction can be excluded from the model evaluation process to avoid culling GCMs that can replicate other climate features and observed teleconnections. More generally, the results presented here can be used as a reference for regional studies and GCM selection for dynamical downscaling, while highlighting the difficulty in constraining precipitation and temperature projections.