Sun, Liqiang, Keith W Dixon, Kenneth E Kunkel, Xia Sun, and David R Easterling, May 2026: Comparative assessment of LOCA2 and STAR-ESDM downscaled surface temperature over the conterminous United States. Journal of Applied Meteorology and Climatology, 65(5), DOI:10.1175/JAMC-D-25-0176.1633-650. 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.
Ullrich, Paul A., Daniel Feldman, Jay Alder, Jen Bewley, Melissa Bukovsky, Keith W Dixon, Ethan D Gutmann, Kripa Jagannathan, Andrew Jones, Rao Kotamarthi, Hugo Lee, Fred Lipschultz, Jeremy Littell, Elias Massoud, Rachel R McCrary, Richard H Moss, Stefan Rahimi-Esfarjani, Andrew Schwarz, Tanya L Spero, Scott Steinschneider, Liqiang Sun, and Adrienne M Wootten, in press: Advancing a coordinated strategy for decision-relevant climate data products for the United States. Bulletin of the American Meteorological Society. DOI:10.1175/BAMS-D-24-0303.1. April 2026. Abstract
Climate adaptation practitioners, planners, and policymakers rely on historical reconstructions and future projections of regional to local climate for adaptation, resilience, and risk management. To serve the many needs of these users, climate data must be relevant for informing the decision-making process (“salient”), consistent with our physical understanding of the global Earth system (“credible”), backed by expert judgment (“authoritative”), and publicly available and ready for use (“accessible”). As more decision-relevant climate data products are being developed to address outstanding needs, important challenges have emerged around the selection, evaluation, use, and standardization of these data products. This article examines opportunities and challenges associated with regional climate data for the United States and describes nascent efforts to build a coordinated strategy among communities of producers and users to maximize the utility of decision-relevant datasets. A community of practice (CoP) is now emerging to benefit both end-users and data producers. The CoP will enable enhanced evaluation protocols, improved uncertainty quantification, and eased data distribution and usage, informed by ongoing input on data needs and feedback on quality and usability of data from a wide range of user communities. Broad coordination among interested parties and periodic reviews of the state of the field are vital to the success of this community of practice.
