The projection of reference crop evapotranspiration (ETo) based on global climate models (GCMs) is an important task in many fields under climate change background. However, ETo projections are typically hindered by the limited data availability or high uncertainty of the GCM-projected climate variable for ETo calculation (e.g., radiation and wind speed). In the current study, we investigate how the selection of climate variables for ETo calculation would influence the reliability and uncertainty of the ETo projections. Based on the Penman-Monteith (P-M) formula and the missing variable estimation approaches provided by FAO, five variable selection schemes (VSs) that select different subsets of the climate variables for ETo calculation are established. Four emission scenarios, five GCMs and four statistical downscaling approaches were adopted, and their uncertainty contributions were quantified by analyses of variation (ANOVA) approach. Results show that, among the climate variables except air temperature, shortwave radiation caused the lowest uncertainty to the projected ETo, and is thus suggested as the most suitable variable to be considered for ETo projections. In most areas, wind speed and relative humidity are determined as marginal variables for their limited influences in ETo projections, yet in some complex-terrain regions, wind speed could propagate considerable uncertainty to the projected ETo. Differences in GCMs is the major source of uncertainty in ETo projections, while the emission scenario generally ranked as second. Statistical downscaling approaches contribute limited uncertainty (below 10%) to ETo projections. Above all, this study shows the applicability of using the missing value estimation approaches for the FAO56 P-M formula to perform future ETo projection, for the practices with limited GCM data availability, and can serve as the reference for selecting suitable variables for ETo forecasting in applications.

基于全球气候模型（GCM）的参考作物蒸散量（ETo）的预测是气候变化背景下许多领域的重要任务。然而，ETo 预测通常受到有限数据可用性或 GCM 预测的用于 ETo 计算的气候变量（例如辐射和风速）的高度不确定性的阻碍。在当前的研究中，我们调查了 ETo 计算气候变量的选择将如何影响 ETo 预测的可靠性和不确定性。基于 Penman-Monteith (PM) 公式和 FAO 提供的缺失变量估计方法，建立了五个变量选择方案 (VSs)，它们选择气候变量的不同子集进行 ETo 计算。采用了四种排放情景、五种 GCM 和四种统计降尺度方法，并且它们的不确定性贡献通过变异分析（ANOVA）方法进行量化。结果表明，在除气温以外的气候变量中，短波辐射对 ETo 预测的不确定性最低，因此被认为是 ETo 预测中最适合考虑的变量。在大多数地区，风速和相对湿度被确定为边缘变量，因为它们对 ETo 预测的影响有限，但在一些复杂地形地区，风速可能会将相当大的不确定性传播到预计的 ETo。GCM 的差异是 ETo 预测不确定性的主要来源，而排放情景通常排在第二位。统计降尺度方法对 ETo 预测的不确定性有限（低于 10%）。首先，