The boreal summer intraseasonal oscillation (BSISO) is one of the dominant modes of intraseasonal variability of the tropical climate system, which has prominent northward propagation extending much further from the equator. The impacts of BSISO on extreme rainfall in eastern China were studied using the BSISO indices and daily rainfall data in China. We revealed that the responses of extreme rainfall to BSISO activity in eastern China are not spatially uniform. Under the influences of BSISO1, the probability‐distribution functions of rainfall in two southeastern China subregions: South China and Yangtze River Valley significantly skew toward larger values, respectively, in phases 4 and 8, and phases 3 and 4 with the probability of 90th extremes increased by 35–45% relative to May–August rainfall probability‐distribution function, showing southward propagation with the speed of 1.96°/phase. Under the BSISO2, the probability of the 90th extremes increased more than 40% in South China and Yangtze River Valley, respectively, during phases 4–5 and phases 6–7, showing northward propagation with a speed of 2.75°/phase. Physical analysis showed that the increased probability of extreme rainfall is associated with intensifying moisture convergence and upward moisture transport during BSISO active phases. The hindcasts from the Climate Forecast System version 2 have been used to evaluate the modulations of BSISO on extreme rainfall and associated predictability. It was shown that the Climate Forecast System version well reproduces the modulations of BSISO on extreme rainfall within 2 weeks. These results demonstrate the feasibility to develop medium‐to‐extended‐range probabilistic forecasts of extreme rainfall for southeastern China.

The impact of Madden–Julian oscillation (MJO) upon extreme rainfall in southern China was studied using the Real-time Multivariate MJO (RMM) index and daily precipitation data from high-resolution stations in China. The probability-distribution function (PDF) of November–March rainfall in southern China was found to be skewed toward larger (smaller) values in phases 2–3 (6–7) of MJO, during which the probability of extreme rainfall events increased (reduced) by 30–50% (20–40%) relative to all days in the same season. Physical analysis indicated that the favorable conditions for generating extreme rainfall are associated with southwesterly moisture convergence and vertical moisture advection over southern China, while the direct contributions from horizontal moisture advection are insignificant. Based on the above results, the model-based predictability for extreme rainfall in winter was examined using hindcasts from the Climate Forecast System version 2 (CFSv2) of NOAA. It is shown that the modulations of MJO on extreme rainfall are captured and forecasted well by CFSv2, despite the existence of a relatively small bias. This study suggests the feasibility of deriving probabilistic forecasts of extreme rainfall in southern China based on RMM indices.

This study conducts an intercomparison of the column-integrated moist static energy (MSE) and water vapor budget of the Madden–Julian oscillation (MJO) among six modern global reanalysis products (RAs). Inter-RA differences in the mean MSE, MJO MSE anomalies, individual MSE budget terms, and their relative contributions to the propagation and maintenance of MJO MSE anomalies are examined. Also investigated is the relationship between the MJO column water vapor (CWV) budget residuals with the other CWV budget terms as well as with the two parameters that characterize cloud–radiation feedback and moisture–convection coupling. Results show a noticeable inter-RA spread in the mean-state MSE, especially its vertical structure. In all RAs, horizontal MSE advection dominates the propagation of the MJO MSE while column-integrated longwave radiative heating and vertical MSE advection are found to be the key processes for MJO maintenance. The MSE budget terms directly affected by the model parameterization schemes exhibit high uncertainty. The differences in anomalous vertical velocity mainly contribute to the large differences in vertical MSE advection among the RAs. The budget residuals show large inter-RA differences and have nonnegligible contributions to MJO maintenance and propagation in most RAs. RAs that underestimate (overestimate) the strength of cloud–radiation feedback and the convective moisture adjustment time scale tend to have positive (negative) MJO CWV budget residual, indicating the critical role of these processes in the maintenance of MJO CWV anomalies. Our results emphasize that a correct representation of the interactions among moisture, convection, cloud, and radiation is the key for an accurate depiction of the MJO MSE and CWV budget in RAs.

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.

This study delves into the once-in-a-century extreme precipitation events in the northern region of the Pearl River Basin during the 2022 Dragon Boat Festival period. Through a comprehensive analysis spanning various temporal scales, from synoptic-scale systems to subseasonal oscillations, including the rare triple-peaked La Niña phenomenon, we illuminate the intricate interactions among these factors and their impact on extreme precipitation events. Specifically, we present a conceptual model of multiscale interaction systems contributing to extreme precipitation in the BeiJiang Basin. Our findings reveal that, during the 2022 Dragon Boat Festival period, precipitation in the BeiJiang Basin exhibited characteristics across multiple time scales, with the synoptic-scale environment proving highly conducive. Systems such as the South Asian High, Western Pacific Subtropical High, and South China Sea summer monsoon were identified as the direct influencing factors of precipitation. Importantly, our study highlight the pivotal role of subseasonal oscillation propagation stagnation in extreme precipitation in the BeiJiang Basin, with synoptic-scale systems playing a contributing role. We emphasize the indirect influence of ENSO signals, regulating not only monsoons but also the propagation of subseasonal oscillations. The interplay of these factors across different temporal scales significantly impacts flood hazards. Overall, our study significantly enhances the understanding of mechanisms driving extreme precipitation events in the Pearl River Basin, with profound implications for water resource management and disaster prevention.