Statistical post-processing can calibrate the individual components of an ensemble forecast but may also distort relationships between forecast dimensions. This paper proposes Score-Optimized Copula Coupling, which rearranges calibrated forecast samples according to an optimized dependence structure to restore potentially lost dependencies. We assess fixed and adaptively updated versions using wind-speed and temperature forecasts. In the variable-specific spatial case studies, all versions significantly outperform the strongest benchmarks, with improvements of 1.08-2.02 percent in energy score and 5.45-7.97 percent in variogram score. A third case study at 24 co-located stations shows that one optimized structure can represent both spatial and inter-variable relationships. The largest energy and variogram score improvements are 3.98 and 3.69 percent for spatial dependence and 1.16 and 1.06 percent for inter-variable dependence; all are statistically significant. Additional calibration terms improve covariance representation but can reduce gains in the other scores.

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