From bilinear interpolation to machine learning: a comparative assessment of statistical downscaling methods for CMIP6 projections over Brazil

High-resolution climate projections are essential for climate impact, vulnerability, and adaptation studies, particularly over regions with strong spatial heterogeneity such as Brazil. Although CMIP6 global climate models (GCMs) provide valuable information on future climate change, their coarse spatial resolutions, typically ranging from 100 to 200 km, limit their direct application at regional and local scales. Statistical downscaling techniques offer computationally efficient alternatives to dynamical downscaling, but their relative performance and added value remain insufficiently assessed over Brazil.

In this study, we compare two statistical downscaling approaches applied to a subset of CMIP6 models previously evaluated by Bazanella et al. (2024) – 10.1007/s00382-023-06979-1 – and identified as skillful in representing Brazilian climate: (i) a bilinear interpolation method followed by percentile-to-percentile bias correction, and  (ii) machine learning–based downscaling approaches. The original GCM outputs are interpolated to a common high-resolution grid of 10 km × 10 km using bilinear weights, providing a physically consistent reference framework. In parallel, ML-based models are trained using historical GCM predictors and high-resolution reference climate datasets to learn nonlinear relationships and generate high-resolution climate fields.

The performance of both approaches is evaluated for the historical period in terms of mean climatology, spatial patterns, and variability. Future projections under the SSP2-4.5 and SSP5-8.5 scenarios are then analyzed to assess regional climate change signals and associated uncertainties. Results assess the extent to which ML-based downscaling provides added value relative to bilinear interpolation, particularly for variables with strong spatial heterogeneity, such as precipitation and temperature extremes, while also evaluating the ability of the approach to preserve the large-scale climate signals projected by the driving CMIP6 models. This comparative analysis provides insights into the applicability, robustness, and limitations of statistical and ML-based downscaling methods for regional climate assessments over Brazil.