During the 2020/2021 winter season, three nationwide cold waves took place from 28 to 31 December 2020, as well as from 5 to 8 January and 14 to 17 January 2021. These cold waves resulted in extreme cold weather in northern and eastern China. In this study, the common features of these cold waves were analyzed, and the key factors contributing to cold waves were illustrated, and the performance of the CMA-GEPS numerical model was evaluated in predicting the cooling effect of the cold waves, and its predictability source was discussed. The results indicated that the cold waves were caused by synergistic effects in the mid- to high-latitude atmospheric circulation of both the upper and lower atmosphere, including polar vortex splitting, enhancement of blocking high, and increased meridional circulation anomaly in the Siberian high area. During the time of cold waves, the mid- to high-latitude atmospheric circulation was undergoing low-frequency adjustment, with the Arctic oscillation continuously weakening, while the blocking high and Siberian high gradually increased to historically high-intensity states. The outbreaks of the three cold waves occurred at the peak and declining points of the blocking high and Siberian high, respectively, acting as short- to medium-term forecast factors. The CMA-GEPS model demonstrated high forecasting ability for the cooling of the cold waves due to its ability to accurately predict the evolution of the Siberian high and blocking high prior to and after the cold wave with a long lead time. Predictability analysis suggested the strong variability of key factors (such as the Siberian high and blocking) in cold wave events may benefit the model’s prediction of cold wave events. These findings contribute to the understanding of the physical mechanisms behind cold waves and the potential for improved forecasting of extreme cold weather events.

The Madden-Julian Oscillation (MJO) has a significant impact on global weather and climate and can be used as a predictability resource in extended-term forecasting. We evaluate the ability of the Chinese Academy of Meteorological Sciences Climate System Model (CAMS-CSM) to represent the MJO by using the diagnostic method proposed by the US Climate Variability and Predictability Program (CLIVAR) MJO Working Group (MJOWG). In general, the model simulates some major characteristics of MJO well, such as the seasonality characteristics and geographical dependence, the intensity of intraseasonal variability (ISV), dominant periodicity, propagation characteristics, coherence between outgoing longwave radiation (OLR) and wind, and life cycle of MJO signals. However, there are a few biases in the model when compared with observational/reanalyzed data. These include an overestimate of precipitation in the convergence zone of the North and South Pacific, a slightly weaker eastward propagation, and a shift in the dominant periodicity toward lower frequencies with slower speeds of eastward propagation. The model gives a poor simulation of the northward propagation of MJO in summer and shows less coherence between the MJO convection and wind. The role of moistening in the planetary boundary layer (PBL) in the eastward/northward propagation of MJO was also explored. An accurate representation of the vertical titling structure of moisture anomalies in CAMS-CSM leads to moistening of the PBL ahead of convection, which accounts for the eastward/northward propagation of MJO. Poor simulation of the vertical structure of the wind and moisture anomalies in the western Pacific leads to a poor simulation of the northward propagation of MJO in this area. Budget analysis of the PBL integral moisture anomalies shows that the model gives a good simulation of the moisture charging process ahead of MJO convection and that the zonal advection of moisture convergence term has a primary role in the detour of MJO over the Maritime Continent.

Boreal summer precipitation over the Tibetan Plateau (TP) is difficult to predict in current climate models and has become a challenging issue. To address this issue, a new analog-based correction method has been developed. Our analysis reveals a substantial correlation between the prediction errors of TP summer precipitation (TPSP) and previous February anomalies of sea surface temperature (SST) in the key regions of tropical oceans. Consequently, these SST anomalies can be selected as effective predictors for correcting prediction errors. With remote-sensing-based and observational datasets employed as benchmarks, the new method was validated using the rolling-independent validation method for the period 1992–2018. The results clearly demonstrate that the new SST analog-based correction method of dynamical models can evidently improve prediction skills of summer precipitation in most TP regions. In comparison to the original model predictions, the method exhibits higher skills in terms of temporal and spatial skill scores. This study offers a valuable tool for effectively improving the TPSP prediction in dynamical models.

In boreal summer, variations of intensity and location of the western North Pacific subtropical high (WNPSH) have significant impacts on weather and climate in East Asia. In this study, the medium-range prediction of WNPSH is comprehensively evaluated with various scores based on reforecast data of the National Centers for Environmental Prediction - Global Ensemble Forecast System, and the predictability source of the WNPSH medium-range forecasting is further analyzed by examining how well the model can reproduce the modulation of El Niño-Southern Oscillation (ENSO) on WNPSH as observed. The results show that this system has a systematic bias in the WNPSH forecasts, mainly manifested by the weak strength and the southeastward shifted position, and such a bias further increases with lead time. Effective prediction skills of WNPSH are 10–11 days for its intensity and area, 7 days for its ridge line, but only 1–3 days for its western boundary ridge point in terms of different scores, respectively, which can be improved through developing a bias correction method of prediction. It is demonstrated that the medium-range predictability of WNPSH is mainly originated from ENSO and its significant lagged effects on WNPSH well revealed in observation can be realistically reproduced by this system within the effective prediction lengths. A strong ENSO modulation of the WNPSH prediction skills has been clearly found in terms of the different indices, which depends on the ENSO’s developing and decaying phases. The intensity and area of WNPSH are usually highly predictable due to the ENSO effect being reproduced by the system well while the location indices of WNPSH have relatively low predictability, which are mainly affected by internal variability and difficultly captured by the model. Predictability analysis of WNPSH as modulated by ENSO shows good potential for medium-range forecasting with high skills.

Ensemble forecast plays a vital role in numerical weather prediction. Hence, effectively extracting useful information from ensemble members to improve precipitation forecasting skills has always been an important issue. Using the ensemble forecast data on precipitation from the ECMWF-GEPS (Global Ensemble Prediction System), we propose a stepwise correction method, based on segmented hierarchical clustering (SHC), for forecast of daily precipitation. This method employs a segmented correction scheme, thereby generating more probabilistic forecast information and improving forecasts. Validations of the SHC method have been performed by comparison with two other methods, namely the ensemble-mean (EM) method and the direct hierarchical clustering (HC) method. Our results showed that deterministic forecast via SHC improved the ability to forecast heavy precipitation in short- and medium-range forecast timeframes. Therefore, SHC performed better than either EM or HC by effectively extending lead time to impending severe rainfall by 2–3 days relative to the other two methods. SHC also demonstrated better performance than the other methods through continuous forecast verification in summer 2021, and even had better effects in the forecast of multiple heavy-precipitation cases, including the Zhengzhou extreme rainfall on 20 July 2021. Overall, the SHC method has great potential for improving ensemble rainfall forecasts in the current operational system.