Sub-seasonal Prediction Skill: is the Mean State a Good Model Evaluation Metric?

Evaluating forecast models encompasses assessing their ability to accurately depict observed climate states and predict future climate variables. Various evaluation methods, from computationally efficient measures like the anomaly correlation coefficient to more intricate approaches, have been formulated. While simpler methods provide limited information, climatology, due to its simplicity and immediate linkage to model performance, is a commonly utilized primary evaluation metric. In this study focusing on temperature and precipitation, we propose a novel metric based on the model’s mean state, integrating both climatology and the seasonal cycle for a more accurate assessment of the relationship between mean state performance and prediction skill on weather and sub-seasonal time scales compared to relying solely on climatology. This integrated metric reveals a robust correlation between temperature and precipitation across diverse geographical locations, with a more pronounced effect in tropical areas when considering the seasonal cycle. Additionally, we find that temperature exhibits higher prediction skill compared to precipitation. The discovered relationship serves as a potential early indicator for predicting the efficacy of Seasonal to Sub-seasonal (S2S) models and offers valuable insights for model development, emphasizing the significance of this integrated metric in enhancing S2S model performance and advancing climate prediction capabilities.