  Stagnant weather condition is one of the major contributors to air pollution
as it is favorable for the formation and accumulation of pollutants. To measure
the atmosphere's ability to dilute air pollutants, Air Stagnation Index (ASI)
has been introduced as an important meteorological index. Therefore, making
long-lead ASI forecasts is vital to make plans in advance for air quality
management. In this study, we found that autumn Ni\~no indices derived from sea
surface temperature (SST) anomalies show a negative correlation with wintertime
ASI in southern China, offering prospects for a prewinter forecast. We
developed an LSTM-based model to predict the future wintertime ASI. Results
demonstrated that multivariate inputs (past ASI and Ni\~no indices) achieve
better forecast performance than univariate input (only past ASI). The model
achieves a correlation coefficient of 0.778 between the actual and predicted
ASI, exhibiting a high degree of consistency.
