Reliable long-term forecasting of Earth system dynamics is fundamentally limited by instabilities in current artificial intelligence (AI) models during extended autoregressive simulations. These failures often originate from inherent spectral bias, leading to inadequate representation of critical high-frequency, small-scale processes and subsequent uncontrolled error amplification. Inspired by the nested grids in numerical models used to resolve small scales, we present TritonCast. At the core of its design is a dedicated latent dynamical core, which ensures the long-term stability of the macro-evolution at a coarse scale. An outer structure then fuses this stable trend with fine-grained local details. This design effectively mitigates the spectral bias caused by cross-scale interactions. In atmospheric science, it achieves state-of-the-art accuracy on the WeatherBench 2 benchmark while demonstrating exceptional long-term stability: executing year-long autoregressive global forecasts and completing multi-year climate simulations that span the entire available $2500$-day test period without drift. In oceanography, it extends skillful eddy forecast to $120$ days and exhibits unprecedented zero-shot cross-resolution generalization. Ablation studies reveal that this performance stems from the synergistic interplay of the architecture's core components. TritonCast thus offers a promising pathway towards a new generation of trustworthy, AI-driven simulations. This significant advance has the potential to accelerate discovery in climate and Earth system science, enabling more reliable long-term forecasting and deeper insights into complex geophysical dynamics.

翻译：当前人工智能模型在扩展自回归模拟中的不稳定性从根本上限制了地球系统动力学的可靠长期预测。这些失效通常源于固有的频谱偏差，导致对关键的高频、小尺度过程表征不足，进而引发不受控制的误差放大。受数值模型中用于解析小尺度的嵌套网格启发，我们提出了TritonCast。其设计的核心是一个专用的潜在动力核心，确保宏观演化在粗尺度上的长期稳定性。外部结构随后将这一稳定趋势与细粒度的局部细节相融合。该设计有效缓解了由跨尺度相互作用引起的频谱偏差。在大气科学中，它在WeatherBench 2基准测试上达到了最先进的精度，同时展现出卓越的长期稳定性：能够执行长达一年的自回归全球预测，并完成跨越整个可用$2500$天测试期的多年气候模拟而无漂移。在海洋学中，它将有效的涡旋预测延长至$120$天，并展现出前所未有的零样本跨分辨率泛化能力。消融研究表明，这一性能源于架构核心组件的协同相互作用。因此，TritonCast为新一代可信赖的人工智能驱动模拟提供了一条有前景的途径。这一重要进展有望加速气候和地球系统科学的发现，实现更可靠的长期预测和对复杂地球物理动力学的更深入理解。