Atmospheric rivers (ARs) are characterized by intense lower tropospheric plumes of moisture transport that are frequently responsible for midlatitude wind and precipitation extremes. The prediction of ARs at subseasonal-to-seasonal (S2S) timescales is currently at a low level of skill, reflecting a need to improve our understanding of their underlying sources of predictability. Based on 20 year hindcast experiments from the Geophysical Fluid Dynamics Laboratory’s SPEAR S2S forecast system, we evaluate the S2S prediction skill of AR activities in the northern winter. Higher forecast skill is detected for high-frequency AR activities (3–7 days/week) compared to low-frequency AR activities (1–2 days/week), even though the occurrence rate of high-frequency ARs exceeds that of low-frequency ARs. For the first time, we have applied the Average Predictability Time technique to the SPEAR system to identify the three most predictable modes of AR in the North Pacific sector. These modes can be attributed to the influences of the El Niño–Southern Oscillation, the Pacific North American pattern, and the Arctic Oscillation. S2S AR forecast skill in western United States is modulated by various phases of large-scale variability. This study highlights potential windows of opportunity for operational S2S AR forecasting.

大气河流 （AR） 的特点是强烈的对流层低层水汽输送羽流，这经常导致中纬度风和降水极端事件。在次季节到季节 （S2S） 时间尺度上对 AR 的预测目前处于较低技能水平，这反映了我们需要提高我们对其潜在可预测性来源的理解。基于地球物理流体动力学实验室 SPEAR S2S 预报系统 20 年的后报实验，我们评估了北方冬季 AR 活动的 S2S 预报技能。与低频 AR 活动（1-2 天/周）相比，高频 AR 活动（3-7 天/周）的预报技能更高，即使高频 AR 的发生率超过低频 AR。我们首次将平均可预测时间技术应用于 SPEAR 系统，以确定北太平洋地区三种最可预测的 AR 模式。这些模态可归因于厄尔尼诺-南方涛动、北美太平洋模式和北极涛动的影响。美国西部的 S2S AR 预报技能受大规模变率的各个阶段的调节。本研究重点介绍了运营 S2S AR 预测的潜在机会窗口。