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 Tukey g-and-h (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：地球系统模型是理解地球复杂气候系统的基石。然而，ESM模拟的巨大计算与存储需求限制了其实际应用。针对新发布的CESM2-LENS2数据所面临的这一挑战，我们提出了一种新颖的随机生成器作为CESM2的实用补充工具，能够快速生成与训练模拟高度吻合的仿真数据。该生成器利用球谐变换将数据从空间域转换到谱域，从而实现高效的低秩近似，显著降低了计算与存储成本。通过考虑轴对称性并为陆地与海洋区域保留不同的秩，我们的生成器能够捕捉复杂的非平稳空间依赖性。此外，改进的Tukey g-and-h变换能够适应高时间分辨率数据中的非高斯特性。我们将所提出的随机生成器应用于CESM2-LENS2数据，在不同尺度上生成了地表温度模拟的仿真数据，这是首次对日尺度数据进行复现尝试。这些仿真数据随后与训练模拟进行了细致验证。本研究为克服计算与存储限制、实现高效气候建模与分析提供了一条具有前景的补充路径。