Predictability and Prediction of Low-Frequency Rainfall Over the Lower Reaches of the Yangtze River Valley on the Time Scale of 20 to 30ￂ days

This paper presents a predictability study of the 20-30-day low-frequency rainfall over the lower reaches of the Yangtze River valley (LYRV). This study relies on an extended complex autoregressive (ECAR) model method, which is based on the principal components of the global 850 hPa low-frequency meridional wind. ECAR is a recently advanced climate forecast method, based on data-driven models. It not only reflects the lagged variations information between the leading low-frequency components of the global circulation and rainfall in a complex space, but also displays the ability to describe the synergy variations of low-frequency components of a climate system in a low dimensional space. A 6-year forecast experiment is conducted on the low-frequency rainfall over the LYRV for the extended-range daily forecasts during 2009-2014, based on the time-varying high-order ECAR. These experimental results demonstrate that the useful skills of the real-time forecasts are achieved for an extended lead-time up to 28 days with a fifth-order model, and are also shown to be 27-day lead for forecasts which are initiated from weak intraseasonal oscillation (ISO). This high-order ECAR displays the ability to significantly improve the predictions of the ISO. The analysis of the 20-30-day ISO predictability reveals a predictability limit of about 28-40 days. Therefore, the forecast framework used in this study is determined to have the potential to assist in improving the real-time forecasts for the 20-30-day oscillations related to the heavy rainfall over the LYRV in summer. Plain Language Summary In recent years, the study of extended-range forecasting has become not only a hot topic of global meteorological research but also a difficulty. It is necessary to study the forecast methods and models, as well as the predictability, from multiple perspectives and angles. Using a climate forecast method that is based on data-driven modeling is one of the main ways to extend the lead time in the extended range. This paper presents that the forecast skill specific to 20 to 30 day oscillation affected the heavy rainfall process in the lower reaches of Yangtze river valley for a suite of the extended complex autoregressive model (ECAR) models, which had a good forecast skill at the lead time of approximately 28 days. It has a forecast ability far superior to the traditional autoregressive model. These ECAR models are based on the major lagged correlations with multiple different laggi