Evaluating CMIP6 models under different statistical downscaling methods for climate assessments in the north of Chile

Abstract

Statistical Downscaling Methods (SDMs) play a pivotal role in climate change assessments at local and regional scales, as they can efficiently reproduce historical climate observations, overcoming the limitation of Global Climate Models (GCMs) in capturing fine-scale climatic features. However, the evaluation of GCMs and SDMs often focuses on historical climatology, neglecting extreme events representation and climate change signal preservation. In response, this paper proposes a methodological guideline for GCMs and SDMs selection, incorporating three key criteria: representation of historical climatology (Past Performance Index - PPI), representation of extreme wet climate indices (Climate Integrated Impact Index - CI3), and preservation of climate signal change (Climate Signal Performance Criteria - SCPI). Satisfactory GCM and SDM performance during the historical period is defined by meeting conditions such as PPI ≥ 0.5 for each climatic variable (precipitation, minimum and maximum temperature) and CI3 ≥ 0.4. For future projections, SCPI guides the selection process, considering short (2015 – 2040), medium (2041 – 2070), and long-term (2071 – 2100) projections across different Shared Socioeconomic Pathways (SSPs) (see step d) in Figure 1).   The study evaluates 18 GCMs from Sixth Model Intercomparison Phase (CMIP6), interpolated to the gridded meteorological product CR2METv2.0 (0.05° x 0.05°) for the northern region of Chile (17ºS – 32º). Ten SDMs are applied to short, medium, and long-term periods under SSP2-4.5 and SSP5-8.5 scenarios. Results indicate that no single SDM corrects all criteria for all GCMs. Climate projection groups are established based on the number of criteria met, distinguishing models that satisfy two or three criteria. The historical evaluation shows that interannual variability is the most influential in the PPI results, both for precipitation and temperatures (min and max). Better historical performance is also observed for multivariate methods family over quantile mapping family or hybrid methods family (combination of analogs, resampling, climate fingerprinting and quantile mapping). In the case of CI3, all SDMs for all the GCMs show a similar bias for maximum precipitation magnitude and their mean temperature, meanwhile the consecutive wet days, days with precipitation over 50 mm and snow process indices present a bias of less than 10%. For this metric, no SDM family has a better performance over another SDM family. Finally, the preserving of climate signal change (for each SSP scenario and projection period) is not observed with the hybrid method. For quantile methods, we observed a tendency of modification of the signal climate change, and the multivariate methods has the best performance in these criteria. This proposed methodology facilitates the selection of GCM subsets based on study objectives (climatology, extreme events, or climate change signals). Future work should focus on advancing additional statistical downscaling methods capable of representing diverse criteria, including natural variability and climate change signals. Figure 1. Methodological scheme for the selection of suitable GCMs and SDMs.