Body mass is a fundamental biological parameter for inferring flight performance, physiological constraints, and ecological niches in early birds. However, estimating body mass in Mesozoic birds remains challenging because fossils are often affected by taphonomic compression, deformation, and incomplete skeletal preservation. Here, we developed both univariate and multivariate regression models based on a dataset of 765 extant bird specimens to identify optimal predictors and evaluate model performance. Results show that humeral midshaft width (HW) provides the strongest predictive power among univariate models; after outlier screening of the dataset (N = 745), the HW-based univariate model achieved an R2 of 0.941. Multivariate regression further improves model fit (adjusted R2 = 0.967, N = 745) and reduces both mean prediction bias (%MPE from 7.5% to 4.1%) and prediction error magnitude [mean(|%PE|) from 31.6% to 22.0%]. Applying these models to Mesozoic bird taxa, we generated refined body-mass estimates, compared clade-level patterns and model outputs, and benchmarked our results against published models. Together, these analyses support multivariate regression as a more robust framework for paleobiological mass estimation and provide a firmer basis for future studies of early avian body-size evolution.

体重是推断早期鸟类飞行能力、生理约束与生态位的重要参数。由于中生代鸟类化石常受到二维压扁、骨骼变形及关键骨骼缺失等埋藏学因素的影响，其体重估算仍存在较大不确定性。基于765件现生鸟类标本数据，建立并比较了单变量与多变量回归模型，通过分析模型拟合度与预测稳定性，筛选了最佳骨骼代理指标，并系统评估不同模型的预测性能。结果显示，在单变量模型中，肱骨中段宽度(HW)具有最强的预测能力(经异常值筛查后R² = 0.941, N = 745); 多变量模型进一步提高了模型拟合度 (adjusted R² = 0.967, N = 745), 并有效降低了平均百分比偏差(%MPE: 7.5%→4.1%)及平均绝对百分比误差［mean(|%PE|): 31.6%→22.0%］。将模型应用于中生代鸟类后，可获得更稳健的体重估计结果，为早期鸟类体型演化、生态分异及飞行能力研究提供了可靠的定量基础。