Comparative Assessment of LOCA2 and STAR-ESDM Downscaled Surface Temperature over the Conterminous United States

Abstract For the conterminous United States, we compare two statistically downscaled climate datasets derived from the Coupled Model Intercomparison Project phase 6 (CMIP6) multimodel ensembles: the Localized Constructed Analogs, version 2 (LOCA2), and the Seasonal Trends and Analysis of Residuals–Empirical Statistical Downscaling Model (STAR-ESDM). Evaluating daily maximum (Tmax) and minimum temperature (Tmin), diurnal temperature range (DTR), temperature variability, annual extremes, and projection changes, we find that 1) both datasets demonstrate broad consistency with observations for the historical period and yield similar magnitudes and spatial patterns of projected climate change; 2) each enhances local-scale features relative to raw CMIP6 outputs and effectively corrects large-scale biases, such as the historically underestimated DTR common in CMIP6 simulations; and 3) both reduce intermodel spread in future climate projections compared to the original CMIP6 ensemble. Despite these common strengths, notable differences are observed: 1) Both datasets reflect uncertainties stemming from the observational products used in their training, particularly for Tmin and DTR; 2) LOCA2 produces a stronger fine-scale signal than STAR-ESDM; and 3) the difference between LOCA2 and STAR-ESDM is more pronounced for Tmin than for Tmax. These results underscore that training data and methodology influence downscaled outcomes, with LOCA2 generally better suited for fine-scale impact studies and STAR-ESDM for applications prioritizing regional coherence. Significance Statement Localized Constructed Analogs, version 2 (LOCA2), and Seasonal Trends and Analysis of Residuals–Empirical Statistical Downscaling Model (STAR-ESDM) are widely used statistically downscaled Coupled Model Intercomparison Project phase 6 (CMIP6) datasets that provide high-resolution surface temperature projections for regional impact assessments. Although both have been broadly validated, their relative skill in capturing temperature variability, extremes, and diurnal temperature range, as well as differences in projected changes, remain unexamined. This study conducts a focused comparison across the conterminous United States, revealing key differences in spatial patterns, variability, and future change signals that can guide dataset selection and interpretation in climate impact studies.