Future temperature variations under greenhouse gas (GHG) emission scenarios are critical to assess possible impacts on human society and make reasonable mitigation policies. Due to the huge running cost, Earth system models (ESMs) may be difficult to flexibly provide the temperature projections following some specific emission pathways for empirical analysis. This study develops the mean and variability filed emulators in the high-resolution land grids to approximate the temperature behavior conditioned on GHG emissions in ESM. The emulator of mean temperature response is modeled as a function of GHG emissions to represent the expected values for ESM output, and the associated high-dimensional spatial dependence across grid points is estimated by the nearest-neighbor Gaussian process. The variability emulator is constructed with the residuals between the mean temperature response and the ESM output, and the associated space-time correlation structure is decomposed by principal component analysis and discrete Fourier transform. The analysis shows that the emulators trained with the runs of ESM only from part of representative concentration pathways can efficiently reproduce the temperature variations under different emission scenarios. The emulated gridded temperatures would be easily taken for climate impact and risk assessment, and be incorporated in the integrated assessment model for climate policy analysis.

温室气体（GHG）排放情景下未来的温度变化对于评估对人类社会的可能影响并制定合理的缓解政策至关重要。由于巨大的运行成本，地球系统模型（ESM）可能难以根据某些特定的排放路径灵活地提供温度预测以进行经验分析。这项研究开发了高分辨率陆地网格中的均值和可变性仿真器，以近似以ESM中的温室气体排放为条件的温度行为。将平均温度响应仿真器建模为温室气体排放的函数，以表示ESM输出的期望值，并通过最近邻高斯过程估算出跨网格点的相关高维空间依赖性。利用平均温度响应和ESM输出之间的残差构造可变性仿真器，并通过主成分分析和离散傅里叶变换分解相关的时空相关结构。分析表明，仅通过部分代表性浓度途径的ESM进行训练的仿真器可以有效地再现不同排放情景下的温度变化。模拟的网格温度将很容易用于气候影响和风险评估，并将其纳入用于气候政策分析的综合评估模型中。分析表明，仅通过部分代表性浓度途径的ESM进行训练的仿真器可以有效地再现不同排放情景下的温度变化。模拟的网格温度将很容易用于气候影响和风险评估，并将其纳入用于气候政策分析的综合评估模型中。分析表明，仅通过部分代表性浓度途径的ESM进行训练的仿真器可以有效地再现不同排放情景下的温度变化。模拟的网格温度将很容易用于气候影响和风险评估，并将其纳入用于气候政策分析的综合评估模型中。