Subseasonal Prediction of Impactful California Winter Weather in a Hybrid Dynamical‐Statistical Framework.

Atmospheric rivers (ARs) and Santa Ana winds (SAWs) are impactful weather events for California communities. Emergency planning efforts and resource management would benefit from extending lead times of skillful prediction for these and other types of extreme weather patterns. Here we describe a methodology for subseasonal prediction of impactful winter weather in California, including ARs, SAWs and heat extremes. The hybrid approach combines dynamical model and historical information to forecast probabilities of impactful weather outcomes at weeks 1–4 lead. This methodology uses dynamical model information considered most reliable, that is, planetary/synoptic‐scale atmospheric circulation, filters for dynamical model error/uncertainty at longer lead times and increases the sample of likely outcomes by utilizing the full historical record instead of a more limited suite of dynamical forecast model ensemble members. We demonstrate skill above climatology at subseasonal timescales, highlighting potential for use in water, health, land, and fire management decision support. Plain Language Summary: California winter weather can alternate between very wet conditions from atmospheric rivers making landfall along the Pacific coast to hot, dry, and windy conditions brought by Santa Ana winds blowing in from the Southwest interior. Atmospheric rivers are important for water resources while also causing flooding, whereas Santa Ana winds are often associated with wildfire, especially following prolonged dry periods. Preparing for these types of weather events is important for managing resources and protecting life and property, yet reliable forecasts beyond about 7–10 days remain a challenge. We have developed a new prediction system that combines information about approaching atmospheric weather patterns from weather forecast models along with historical information relating those patterns to impacts over California to predict the likelihood of impactful weather at 1–4 weeks lead time. By extending the window of opportunity to take management action, this new approach should aid in resource and emergency planning in water, land, and fire sectors as well as protecting residents through improved warning systems. Key Points: A hybrid dynamical‐statistical model is described for 1–4‐week forecasts of impactful California winter weather using circulation regimesThis hybrid framework reduces the number of forecasts produced, but the ones issued can be interpreted with higher confidenceThis new methodology provides skillful subseasonal forecasts with potential to improve early warnings for impactful weather events [ABSTRACT FROM AUTHOR]