The El Niño–Southern Oscillation (ENSO) provides most of the global seasonal climate forecast skill^1,2,3, yet, quantifying the sources of skilful predictions is a long-standing challenge^4,5,6,7. Different sources of predictability affect ENSO evolution, leading to distinct global effects. Artificial intelligence forecasts offer promising advancements but linking their skill to specific physical processes is not yet possible^8,9,10, limiting our understanding of the dynamics underpinning the advancements. Here we show that an extended nonlinear recharge oscillator (XRO) model shows skilful ENSO forecasts at lead times up to 16–18 months, better than global climate models and comparable to the most skilful artificial intelligence forecasts. The XRO parsimoniously incorporates the core ENSO dynamics and ENSO’s seasonally modulated interactions with other modes of variability in the global oceans. The intrinsic enhancement of ENSO’s long-range forecast skill is traceable to the initial conditions of other climate modes by means of their memory and interactions with ENSO and is quantifiable in terms of these modes’ contributions to ENSO amplitude. Reforecasts using the XRO trained on climate model output show that reduced biases in both model ENSO dynamics and in climate mode interactions can lead to more skilful ENSO forecasts. The XRO framework’s holistic treatment of ENSO’s global multi-timescale interactions highlights promising targets for improving ENSO simulations and forecasts.

厄尔尼诺-南方涛动（ENSO）提供了大部分全球季节性气候预测技能 ^1,2,3 ，然而，量化熟练预测的来源是一个长期存在的挑战 ^4,5,6,7 。不同的可预测性来源会影响 ENSO 的演变，从而导致不同的全球效应。人工智能预测提供了有希望的进步，但将其技能与特定的物理过程联系起来尚不可能 ^8,9,10 ，这限制了我们对支撑这些进步的动力的理解。在这里，我们展示了扩展的非线性补给振荡器 (XRO) 模型在长达 16-18 个月的交付时间内显示出熟练的 ENSO 预测，优于全球气候模型，并且可与最熟练的人工智能预测相媲美。 XRO 简洁地结合了 ENSO 的核心动力学以及 ENSO 与全球海洋其他变化模式的季节性调节相互作用。 ENSO 长期预报技能的内在增强可以通过其他气候模式的记忆和与 ENSO 的相互作用追溯到其他气候模式的初始条件，并且可以根据这些模式对 ENSO 幅度的贡献进行量化。使用经过气候模型输出训练的 XRO 进行的重新预测表明，减少 ENSO 模型动力学和气候模式相互作用中的偏差可以导致更熟练的 ENSO 预测。 XRO 框架对 ENSO 全球多时间尺度相互作用的整体处理凸显了改进 ENSO 模拟和预测的有希望的目标。