Paleoclimate data assimilation effectively integrates climate model simulations with proxy records and has emerged as a key approach for reconstructing past climate. The prior strongly influences assimilation results. However, the literature lacks a systematic comparative framework across diverse model prior and a rigorous quantitative assessment of assimilation efficacy. We applied an offline paleoclimate data assimilation framework to systematically evaluate the influence of prior datasets derived from 13 coupled climate models on annual mean temperature field reconstructions across the Northern Hemisphere over the past millennium. Evaluation over the 1880-2000 observational period revealed consistent improvements in reconstruction quality across both temporal and spatial dimensions: temporal correlation coefficients increased from 0.54~0.88 to 0.86~0.89, Taylor skill scores rose from 0.31~0.78 to 0.71~0.80, and the spread across reconstructions narrowed substantially. Spatially, assimilation enhanced the coherence of interannual variability across the Northern Hemisphere and corrected mean-state biases in most regions. The magnitude of improvement varied regionally, primarily reflecting the combined constraints of proxy network density and prior field quality, with model-inherent biases constituting a key limiting factor for efficiency coefficient gains in specific regions. A multidimensional rank scoring system classified all reconstructions into high-, medium-, and low-performance groups. All groups clearly captured the Medieval Warm Period, the Little Ice Age, and the Modern Warm Period, and reproduced both polar amplification and the cooling signatures of major volcanic eruptions, with the high-performance group demonstrating the greatest robustness. This study confirms that standard Coupled Model Intercomparison Project (CMIP) outputs serve as viable prior datasets for paleoclimate data assimilation, provide a quantitative basis for prior selection, and offer a data foundation for advancing our understanding of climate variability over the past millennium.