Subseasonal prediction fills the gap between weather forecasts and seasonal outlooks. There is evidence that predictability on subseasonal timescales comes from a combination of atmosphere, land, and ocean initial conditions. Predictability from the land is often attributed to slowly varying changes in soil moisture and snowpack, while predictability from the ocean is attributed to sources such as the El Niño Southern Oscillation. Here we use a set of subseasonal reforecast experiments with CESM2 to quantify the respective roles of atmosphere, land, and ocean initial conditions on subseasonal prediction skill over land. These reveal that the majority of prediction skill for global surface temperature in weeks 3–4 comes from the atmosphere, while ocean initial conditions become important after week 4, especially in the Tropics. In the CESM2 subseasonal prediction system, the land initial state does not contribute to surface temperature prediction skill in weeks 3–6 and climatological land conditions lead to higher skill, disagreeing with our current understanding. However, land-atmosphere coupling is important in week 1. Subseasonal precipitation prediction skill also comes primarily from the atmospheric initial condition, except for the Tropics, where after week 4 the ocean state is more important.

次季节预测填补了天气预报和季节展望之间的空白。有证据表明，次季节时间尺度的可预测性来自大气、陆地和海洋初始条件的组合。陆地的可预测性通常归因于土壤湿度和积雪的缓慢变化，而海洋的可预测性则归因于厄尔尼诺南方涛动等来源。在这里，我们使用 CESM2 进行了一组次季节重新预报实验，以量化大气、陆地和海洋初始条件各自对陆地次季节预报技能的作用。这些表明，第 3-4 周全球表面温度的大部分预测技能来自大气，而海洋初始条件在第 4 周后变得重要，尤其是在热带地区。在CESM2次季节预测系统中，陆地初始状态对第3-6周的地表温度预测技能没有贡献，气候土地条件导致更高的技能，与我们目前的理解不一致。然而，陆地-大气耦合在第1周很重要。次季节降水预测技能也主要来自大气初始条件，但热带地区除外，第4周后海洋状态更为重要。