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.

The implementation of elevation bands is a common strategy to account for vertical heterogeneity in hydrology and land surface models; however, there is no consensus guidelines for their delineation. We characterize hydrological implications of this choice by configuring the Variable Infiltration Capacity (VIC) model in nine mountainous basins of the Andes Cordillera, central Chile, using six different setups: no elevation bands (benchmark model), and elevation bands with vertical discretizations of 1000, 750, 500, 200 and 100 m. The analyses are conducted in a wet period (April/1982-March/1987), dry period (April/2010-March/2015) and a climatological period April/1982-March/2015). The results show that adding elevation bands yield little variations in simulated monthly or daily streamflow; however, there are important effects on the partitioning of precipitation between snowfall and rainfall, snowmelt, sublimation, and the spatial variability in September 1 SWE, suggesting a model-structure equifinality. Incorporating elevation bands generally yields less basin-averaged snowmelt, and more (less) catchment-scale sublimation across water-limited (energy-limited) basins. Further, the implications of elevation bands vary with the analysis period: fluxes are more affected during the wet period, while variations in September 1 SWE are more noticeable during the dry period. In general, the effects of adding elevation bands are reduced with increasing vertical discretization, and can differ among catchments. Finally, the grid cells that yield the largest sensitivities to vertical discretization have relatively lower mean altitude, elevation ranges >1000 m, steep slopes (>15°) and annual precipitation amounts <1000 mm, with large intra-annual variations in the water/energy budget.

The assessment of climate change impacts on water resources and flood risk is typically underpinned by hydrological models calibrated and selected based on observed streamflow records. Yet, changes in climate are rarely accounted for when selecting hydrological models, which compromises their ability to robustly represent future changes in catchment hydrology. In this paper, we test a simple framework for selecting an ensemble of calibrated hydrological model structures in catchments where changing climatic conditions have been observed. We start by considering 78 model structures produced using the FUSE modular modelling framework and rely on a Pareto scheme to select model structures maximizing model efficiency in both wet and dry periods. The application of this approach in three case study basins in Peru enables the identification of structures with good robustness, but also good performance according to hydrological signatures not used for model selection. We also highlight that some model structures that perform well according to traditional efficiency metrics have low performance in contrasting climates or suspicious internal states and fluxes. Importantly, the model selection approach followed here helps to reduce the spread in precipitation elasticities and temperature sensitivities, providing a clearer picture of future hydrological changes. Overall, this work demonstrates the potential of using contrasting climatic conditions in a multi-objective framework to produce robust and credible simulations, and to constrain structural uncertainties in hydrological projections.