Seasonal drought predictability and forecast skill over China

Under a changing environment, seasonal droughts have been exacerbated with devastating impacts. However, the understanding of drought mechanism and predictability is limited. Based on the hindcasts from multiple climate models, the predictability and forecast skill for drought over China are investigated. The 3 month standardized precipitation index is used as the drought index, and the predictability is quantified by using a perfect model assumption. Ensemble hindcasts from multiple climate models are assessed individually, and the grand multimodel ensemble is also evaluated. Drought forecast skill for model ensemble mean is higher than individual ensemble members, and North American Multimodel Ensemble grand ensemble performs the best. Predictability is higher than forecast skill, indicating the room for improving drought forecast. Drought predictability and forecast skill are positively correlated in general, but they vary depending on seasons, regions, and forecast leads. Higher drought predictability and forecast skill are found over regimes where ENSO has significant impact. For the ENSO-affected regimes, both drought predictability and forecast skill in ENSO years are higher than that in neutral years. This study suggests that predictability not only provides a measure for selecting climate models for ensemble drought forecast in ENSO-affected regimes but also serves as an indicator for forecast skill especially when in situ and/or remote sensing measurements for the hindcast verifications are considered unreliable.