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Earth system models (ESMs) are fundamental for understanding Earth's complexclimate system. However, the computational demands and storage requirements ofESM simulations limit their utility. For the newly published CESM2-LENS2 data,which suffer from this issue, we propose a novel stochastic generator (SG) as apractical complement to the CESM2, capable of rapidly producing emulationsclosely mirroring training simulations. Our SG leverages the spherical harmonictransformation (SHT) to shift from spatial to spectral domains, enablingefficient low-rank approximations that significantly reduce computational andstorage costs. By accounting for axial symmetry and retaining distinct ranksfor land and ocean regions, our SG captures intricate non-stationary spatialdependencies. Additionally, a modified TGH transformation accommodatesnon-Gaussianity in high-temporal-resolution data. We apply the proposed SG togenerate emulations for surface temperature simulations from the CESM2-LENS2data across various scales, marking the first attempt of reproducing dailydata. These emulations are then meticulously validated against trainingsimulations. This work offers a promising complementary pathway for efficientclimate modeling and analysis while overcoming computational and storagelimitations.

Efficient stochastic generators with spherical harmonic transformation for high-resolution global climate simulations from CESM2-LENS2