We propose a framework to assess monthly GCM precipitation and temperature simulations with the aim of achieving robust annual and seasonal climatic projections. The approach is based on a Past Performance Index (PPI) inspired by the Kling-Gupta Efficiency (KGE) and accounts for climatological averages, interannual variability, seasonal cycle, monthly probabilistic distribution and spatial patterns of climatological means. The PPI formulation is flexible enough to include additional evaluation metrics and weight them differently, enabling the diagnostics and classification of GCMs in a simple diagram that shows the joint performance for precipitation and temperature. We demonstrate the utility of this approach to evaluate 27 CMIP6 models and constrain the spread of projections in five regions with very different climates across continental Chile. We also examine the degree of correspondence between the ensemble of models classified as ‘satisfactory’ based on the PPI and the capability of GCMs to reproduce teleconnection responses to El Niño Southern Oscillation and the Southern Annular Mode. The results show that the approach is useful to discriminate models that do not reproduce the seasonal precipitation cycle and to narrow the spread of projected annual and seasonal changes. The best models, according to the PPI, do not necessarily overlap with those that replicate historically observed teleconnections, suggesting that the latter criterion complements our GCM assessment framework. Finally, we show that model features that can be improved through bias correction can be excluded from the model evaluation process to avoid culling models that reproduce historically observed teleconnections.

Characterizing climate change impacts on water resources typically relies on Global Climate Model (GCM) outputs that are bias-corrected using observational datasets. In this process, two pivotal decisions are (i) the Bias Correction Method (BCM) and (ii) how to handle the historically observed time series, which can be used as a continuous whole (i.e., without dividing it into sub-periods), or partitioned into monthly, seasonal (e.g., three months), or any other temporal stratification (TS). Here, we examine how the interplay between the choice of BCM, TS, and the raw GCM seasonality may affect historical portrayals and projected changes. To this end, we use outputs from 29 GCMs belonging to the CMIP6 under the Shared Socioeconomic Pathway 5–8.5 scenario, using seven BCMs and three TSs (entire period, seasonal, and monthly). The results show that the effectiveness of BCMs in removing biases can vary depending on the TS and climate indices analyzed. Further, the choice of BCM and TS may yield different projected change signals and seasonality (especially for precipitation), even for climate models with low bias and a reasonable representation of precipitation seasonality during a reference period. Because some BCMs may be computationally expensive, we recommend using the linear scaling method as a diagnostics tool to assess how the choice of TS may affect the projected precipitation seasonality of a specific GCM. More generally, the results presented here unveil trade-offs in the way BCMs are applied, regardless of the climate regime, urging the hydroclimate community for a careful implementation of these techniques.