Earth system models (ESMs) are fundamental for understanding Earth's complex climate system. However, the computational demands and storage requirements of ESM simulations limit their utility. For the newly published CESM2-LENS2 data, which suffer from this issue, we propose a novel stochastic generator (SG) as a practical complement to the CESM2, capable of rapidly producing emulations closely mirroring training simulations. Our SG leverages the spherical harmonic transformation (SHT) to shift from spatial to spectral domains, enabling efficient low-rank approximations that significantly reduce computational and storage costs. By accounting for axial symmetry and retaining distinct ranks for land and ocean regions, our SG captures intricate non-stationary spatial dependencies. Additionally, a modified TGH transformation accommodates non-Gaussianity in high-temporal-resolution data. We apply the proposed SG to generate emulations for surface temperature simulations from the CESM2-LENS2 data across various scales, marking the first attempt of reproducing daily data. These emulations are then meticulously validated against training simulations. This work offers a promising complementary pathway for efficient climate modeling and analysis while overcoming computational and storage limitations.

翻译：地球系统模式（ESM）是理解地球复杂气候系统的基础，但其模拟过程中的计算需求与存储限制制约了其应用价值。针对新发布的受此问题影响的CESM2-LENS2数据，我们提出了一种新型随机生成器（SG），可快速生成与训练模拟高度相似的仿真数据，作为CESM2的实用补充方案。该生成器利用球谐变换（SHT）将空间域映射至谱域，通过低秩近似有效降低计算与存储成本。通过考虑轴对称性并保留陆地区域与海洋区域的不同秩数，该生成器可捕捉复杂的非平稳空间依赖性。此外，改进的TGH变换可适应高时间分辨率数据的非高斯特性。我们将所提出的随机生成器应用于CESM2-LENS2数据中不同尺度的地表温度模拟仿真生成，这是首次实现日尺度数据复现。随后，对这些仿真结果与训练模拟进行了严格验证。本研究为突破计算与存储限制下的高效气候模拟与分析提供了一条具有前景的补充路径。