Probabilistic subseasonal precipitation forecasts using preceding atmospheric intraseasonal signals in a Bayesian perspective

Accurate and reliable subseasonal precipitation forecasts are of great socioeconomic value for various aspects. The atmospheric intraseasonal oscillation (ISO), which is one of the leading sources of subseasonal predictability, can potentially be used as predictor for subseasonal precipitation forecasts. However, the relationship between atmospheric intraseasonal signals and subseasonal precipitation is of high uncertainty. In this study, we develop a spatiotemporal-projection-based Bayesian hierarchical model (STP-BHM) for subseasonal precipitation forecasts. The coupled covariance patterns between the preceding atmospheric intraseasonal signals and precipitation are extracted, and the corresponding projection coefficients are defined as predictors. A Bayesian hierarchical model (BHM) is then built to address the uncertainty in the relationship between atmospheric intraseasonal signals and precipitation. The STP-BHM model is applied to predict both the pentad mean precipitation amount and pentad mean precipitation anomalies for each hydroclimatic region over China during the boreal summer monsoon season. The model performance is evaluated through a leave-1-year-out cross-validation strategy. Our results suggest that the STP-BHM model can provide skillful and reliable probabilistic forecasts for both the pentad mean precipitation amount and pentad mean precipitation anomalies at leads of 20-25 d over most hydroclimatic regions in China. The results also indicate that the STP-BHM model outperforms the National Centers for Environmental Prediction (NCEP) subseasonal to seasonal (S2S) model when the lead time is beyond 5 d for pentad mean precipitation amount forecasts. The intraseasonal signals of 850 and 200 hPa zonal wind (U850 and U200) and 850 and 500 hPa geopotential height (H850 and H500) contribute more to the overall forecast skill of the pentad mean precipitation amount predictions. In comparison, the outgoing longwave radiation anomalies (OLRAs) contribute most to the forecast skill of the pentad mean precipitation anomaly predictions. Other sources of subseasonal predictability, such as soil moisture, snow cover, and stratosphere-troposphere interaction, will be included in the future to further improve the subseasonal precipitation forecast skill.