The performance of climate field reconstruction (CFR) and index reconstruction methods is evaluated using proxy and non-informative predictor experiments. The skill of both reconstruction methods is determined using proxy data targeting the western region of North America. The results are compared to those targeting the same region, but derived from non-informative predictors comprising red-noise time series reflecting the full temporal autoregressive structure of the proxy network. All experiments are performed as probabilistic ensembles, providing estimated Monte Carlo distributions of reconstruction skill. Results demonstrate that the CFR skill distributions from proxy data are statistically distinct from and outperform the corresponding skill distributions generated from non-informative predictors; similar relative performance is demonstrated for the index reconstructions. In comparison to the CFR results using proxy information, the index reconstructions exhibit similar skill in calibration, but somewhat less skill in validation and a tendency to underestimate the amplitude of the validation period mean.