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Using the prediction data from the Global Seasonal Forecast version 5 (GloSea5) during the 1993–2016 period, the probabilistic prediction skills of the midsummer (July and August) surface air temperature (SAT) over the middle and lower reach of the Yangtze River valley (MLYR) are evaluated by the ranked probabilistic skill score. We found that the GloSea5 better predicts below-normal (BN) events than above-normal (AN) events at a long lead time, as well as the associated western Pacific subtropical high (WPSH) and circumglobal teleconnection (CGT) anomaly. This result reveals the asymmetry of the probabilistic prediction skills of the midsummer MLYR SAT, which is related to the asymmetric effect of predictability sources: the warm midsummer ENSO favors BN events via promoting the eastward retreat of the WPSH, whereas the cold midsummer ENSO exhibits weak effect on AN events due to the induced southwestward expansion of the WPSH; the warm mid-latitude North Atlantic (MNA) promotes AN events via projecting its influence onto the atmospheric teleconnections such as the CGT, but not vice versa. The GloSea5 exhibits strong response to both two predictability sources, but fails to reproduce the asymmetric effect of predictability sources especially for ENSO, which limits the prediction skills of AN events. On the other hand, the worse (better) simulations of the warm MNA (warm midsummer ENSO) lead to the lower (higher) prediction skills of AN (BN) events. These results are useful for better understanding the predictability of the midsummer SAT over the MLYR. [ABSTRACT FROM AUTHOR]

By using the hindcast and forecast data from the National Centers for Environmental Prediction Climate Forecast System version 2 (NCEP CFSv2) for the 1982–2018 period, we investigate the forecasting skills of the mid‐summer (July and August) surface air temperature (SAT) at interannual timescales in this study. Although CFSv2 predictions show a warm bias for the climatological mean SAT over the Yangtze River valley (25°–32°N, 105°–122°E), they show a consistent and great performance in predicting the interannual variability of the mid‐summer SAT over this region until 4 months in advance, where the linear correlation coefficient between the predicted and observed time series reaches +0.65, +0.51 and + 0.68 for 4, 2 and 0 months in advance, respectively. The CFSv2 predictions well simulate the linkage between the SAT anomalies over the Yangtze River valley and the anomalous atmospheric circulation aloft, including the circumglobal teleconnection and zonal extension of the Western Pacific Subtropical High. However, CFSv2 has trouble in simulating the associated vertical velocity, cloud cover and solar radiation anomalies, except for 0 months in advance. The persistent forecasting skills result from the accurate response of the circumglobal teleconnection and Western Pacific Subtropical High to the El Niño/Southern Oscillation and sea surface temperature (SST) anomalies over the mid‐latitude North Atlantic. Correspondingly, the forecasting skill, signal and signal‐to‐noise ratio are effectively improved in the years with strong mid‐summer SST anomalies over the tropical central‐eastern Pacific or mid‐latitude North Atlantic. These results are useful for understanding the predictability of the mid‐summer SAT over the Yangtze River valley. [ABSTRACT FROM AUTHOR]

This study explores the near‐surface air temperature (TAS) prediction skills at different time scales via coupled climate models that were involved in the Coupled Model Intercomparison Project phase 5 (CMIP5). The simulation skills of the global mean TAS are first assessed between the observations and models; then, the temporal variability is separated into three parts (the linear trend, decadal variability and inter‐annual variability), and each part is compared with the observations. It is found that the global mean TAS anomaly and the decadal variability are well captured by the model, while the inter‐annual variability is poorly presented. In all the three parts of different time scales, there are larger differences among the 18 models, and the ensemble mean of CMIP5 is the closest to the observations. Besides, the TAS decadal variability is better presented for global ocean than for global land. In the assessment of climate oscillation, it is found that the models can well reproduce the TAS decadal variation patterns correlated with the Pacific Decadal Oscillation (PDO) and the inter‐annual variation patterns correlated with El Niño–Southern Oscillation (ENSO) but poor for the decadal variation patterns related to the Atlantic Multidecadal Oscillation (AMO). This study provides a reference assessing the simulation skills of climate models and an indicator evaluating the advantage of CMIP6 in comparison with CMIP5. [ABSTRACT FROM AUTHOR]