Long-term regional climate projections are critical for validating and implementing regional impact assessments and for capturing slow processes in ecological models. Earth System Model (ESM) simulations are the primary source of climate data, but their coarse resolution, systematic biases, and limited representation of uncertainty restrict their applicability. Here, we present DPastCliM-NA, a bias-corrected, high-resolution (0.2° × 0.2°) monthly reconstruction of temperature and precipitation for North America over the past 2000 years, with explicit uncertainty estimates. The dataset is derived by statistically downscaling MPI-ESM1.2-LR simulations against the GHCN-m observational dataset for 1875-2014. Downscaling is performed using Principal Component Regression, and incorporates a model selection strategy to prevent overfitting. Spatiotemporal dependencies in the residuals are captured through spatial error models and autoregressive-moving-average processes. The resulting reconstructions show substantial improvements over raw ESM output, while preserving the long-term climate trends and variability represented by the ESM.
Guaita et al. (Wed,) studied this question.
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