Accurate global medium-range weather forecasting is fundamental to Earth system science. Most existing Transformer-based forecasting models adopt vision-centric architectures that neglect the Earth's spherical geometry and zonal periodicity. In addition, conventional autoregressive training is computationally expensive and limits forecast horizons due to error accumulation. To address these challenges, we propose the Shifted Earth Transformer (Searth Transformer), a physics-informed architecture that incorporates zonal periodicity and meridional boundaries into window-based self-attention for physically consistent global information exchange. We further introduce a Relay Autoregressive (RAR) fine-tuning strategy that enables learning long-range atmospheric evolution under constrained memory and computational budgets. Based on these methods, we develop YanTian, a global medium-range weather forecasting model. YanTian achieves higher accuracy than the high-resolution forecast of the European Centre for Medium-Range Weather Forecasts and performs competitively with state-of-the-art AI models at one-degree resolution, while requiring roughly 200 times lower computational cost than standard autoregressive fine-tuning. Furthermore, YanTian attains a longer skillful forecast lead time for Z500 (10.3 days) than HRES (9 days). Beyond weather forecasting, this work establishes a robust algorithmic foundation for predictive modeling of complex global-scale geophysical circulation systems, offering new pathways for Earth system science.

翻译：准确的全球中期天气预报是地球系统科学的基础。现有大多数基于Transformer的预报模型采用以视觉为中心的架构，忽略了地球的球面几何结构与纬向周期性。此外，传统的自回归训练计算成本高昂，且误差累积限制了预报时效。为应对这些挑战，我们提出了Shifted Earth Transformer（Searth Transformer），这是一种融入物理先验的架构，将纬向周期性和经向边界纳入基于窗口的自注意力机制，以实现物理一致的全球信息交换。我们进一步引入了中继自回归微调策略，使模型能够在有限的内存和计算资源下学习大气的长程演化过程。基于这些方法，我们开发了全球中期天气预报模型YanTian。YanTian在精度上超越了欧洲中期天气预报中心的高分辨率预报，在一度分辨率下与最先进的人工智能模型性能相当，同时所需计算成本约为标准自回归微调方法的1/200。此外，YanTian对Z500的有效预报时效（10.3天）优于HRES（9天）。除天气预报外，本研究为复杂全球尺度地球物理环流系统的预测建模奠定了坚实的算法基础，为地球系统科学开辟了新路径。