As an important approach to improving prediction accuracy,the post-process error correction of climate model products plays an indispensable role in global operational climate systems.To enhance the prediction precision of numerical climate prediction models,this study applies the Convolutional Neural Network(CNN)approach to conduct post-process correction on key operational prediction products of the third-generation climate operational prediction system of the China Meteorological Administration(CMA),i.e.,CMA-CPSv3.The targeted products include monthly 2 m air temperature,precipitation over China,and the El Niño-Southern Oscillation(ENSO)index during the period 2001-2023.Using reanalysis data from the National Centers for Environmental Prediction(NCEP)as the observational benchmark,a dedicated correction model has been developed through deep learning training of a multi-layer CNN architecture.After model construction,changes in the model performance before and after correction are evaluated during an independent test period.Results indicate that the CNN model significantly improves the prediction accuracy of climate model products.For temperature and precipitation predictions in China,the correlation coefficient of 1-7 months lead predictions is increased by 0.1-0.5.Among these improvements,the Root Mean Square Error(RMSE)of temperature is decreased by 0.5-1.0℃,representing a reduction rate of 20%—30%.For precipitation,the correlation coefficient is increased by 0.1-0.2(an increase of 10%—20%),and the RMSE is decreased by 0.1-1.0 mm/d(a reduction rate of 3%—30%),with the RMSE reduction rate reaching 30%—50%in Eastern and Southeastern China.For the ENSO index,the correlation skill for forecasts with a lead time of 1-7 months is enhanced by 5%—7%,and the RMSE at a lead time of 7 months is reduced by 50%,suggesting that the model effectively addresses the issue of excessive oscillation amplitude of the ENSO index in the original CMA-CPSv3 model.Furthermore,this study explicitly identifies a limitation of the CNN model,i.e.,excessive intensity smoothing,when applied to the correction of extreme climate events,and proposes multi-dimensional directions for future optimization.It thus provides a technical solution that integrates scientific rigor and practical applicability for operational post-processing of CMA's climate models.

The ongoing rise in greenhouse gas emissions is leading to a sharp increase in global surface temperatures and more frequent extreme weather events, which has intensified the fluctuation range of daily extreme temperatures and increased the difficulty of prediction. Research on forecasting changes in daily extreme temperature can provide reliable scientific data for assessing future disaster risks and support decision-making. Due to limitations in the performance and sensitivity, current global climate models (GCM) exhibit considerable uncertainty in predicting extreme temperatures, increasing the difficulty of predicting future trends. It is necessary to correct the direct prediction results of GCMs to obtain more reliable prediction results. Therefore, Siberian sea level pressure and the sea surface temperature of the Indian Ocean, both of which have significant impacts on the daily extreme temperature changes in China, are selected as physical factors for correction. Two methods, emergent constraints and Pareto optimal ensemble, are employed to correct GCM' predictions of daily extreme temperature changes in China under the SSP1-2.6 scenario for the middle of the 21st century. A comparison of results before and after correction reveals that both methods could effectively reduce the inter-model uncertainty of future daily extreme temperature changes. Among them, Pareto optimal ensemble scheme, which integrates three-variable factors-daily extreme temperature in China, Siberian sea level pressure, and the Indian Ocean sea surface temperature, proves most effective in minimizing inter-model uncertainty. The range of multi-model predictions of daily maximum (minimum) temperature changes in China for the mid-21st century, as corrected by the three-variable Pareto optimal ensemble scheme, is narrowed to 1.26 ℃ to 2.10 ℃ (1.12℃ to 2.06 ℃). The uncertainty range is reduced by approximately 36.8% (32.9%) compared to the uncorrected results. Moreover, the signal-to-noise ratio of the predicted daily extreme temperature changes increase in most areas of China, rising from below 1 without correction to above 1. At the same time, corrected results based on three-variable Pareto optimal ensemble scheme show significant regional differences, adjusting the magnitude of warming differentially over the Qinghai-Xizang Plateau, Northwest China, and Sichuan Basin. Overall, employing physical constraint derived from selected constraint factors to correct predictions of future daily extreme temperature changes in China is shown to be useful and feasible.

This study assesses the performance of the third generation operational climate prediction system developed by the China Meteorological Administration (CMA-CPSv3) in predicting the Asian summer monsoon on seasonal time scales. The evaluation is carried out using a 20-year set of ensemble hindcast data, which provides a solid foundation for comprehensively examining the model’s predictive capability and reliability. Results from the assessment demonstrate that CMA-CPSv3 has higher predictive skill for key components of the Asian summer monsoon system, covering a wide range of crucial climatic elements. Specifically, the model performs well in predicting the location of the summer rain belt, maximum rainfall intensity and distribution, large-scale atmospheric circulation patterns, the monsoon onset progression, as well as the interannual variability of dynamic summer monsoon indices that reflect the intensity and fluctuation of the monsoon system.Notably, the model can realistically capture the interannual variability of the western North Pacific subtropical high, a pivotal atmospheric circulation system closely associated with the position and movement of the summer rain belt over eastern China. Accurate representation of this variability lays a solid foundation for improving regional rainfall predictions. When compared with its previous version, CMA-CPSv2, the upgraded CMA-CPSv3 exhibits substantial and widespread improvements in summer precipitation prediction across the Asian continent, with particularly remarkable enhancements over eastern China, a region deeply affected by the Asian summer monsoon. Further analysis suggests that these improvements are mainly attributed to the optimized simulations of sea surface temperatures in the tropical Pacific Ocean and Indian Ocean, as well as the strengthened and more realistic ocean–atmosphere coupling processes linked to these tropical sea areas. The refined air-sea interactions enable the model to better depict the remote impacts of tropical oceans on the Asian summer monsoon system, thus elevating the overall accuracy and stability of seasonal climate predictions.

Ensemble prediction has been an important tool for weather forecasting, sub-seasonal to seasonal prediction, seasonal prediction, interannual prediction and even simulation of climate change, which has garnered widespread attention in the field of meteorology. This paper introduces the ensemble prediction scheme of China Meteorological Administration Climate Prediction System version 3 (CMA-CPSv3). In this scheme, we adopt the approach of combining stochastic perturbations of physical process tendencies in the atmosphere and the air-sea flux with time-lagged initial value perturbation. Based upon the version 2 of High-Resolution Beijing Climate Centre Climate System Model (BCC-CSM2-HR), we have developed a multi-layer random perturbation ensemble prediction system with relatively good ensemble sample dispersion, stability, and reliability. Results of evaluation for hindcasts over the past 20 years show that this ensemble prediction system significantly improves the prediction of precipitation and 2 m air temperature over China, as well as the El Niu00F1o-Southern Oscillation (ENSO), Indian Ocean Dipole (IOD) and Asian Monsoon. In particular, the random perturbation of air-sea flux shows a positive effect on improving the prediction skills of ENSO and Southeast Asian Monsoon (SEAM) and Western North Pacific Summer Monsoon (WNPSM) indices. This study offers useful insights for further characterizing uncertainty in other component models of the climate system.

To address the systematic bias in simulating 10-m wind speed using MIROC6 model within CMIP6 framework, a novel approach based on deep learning is proposed. A non-stationary Informer model (Ns-Informer) is integrated with an adaptive-length attention mechanism and a multilayer perceptron (MLP) dynamic adjustment module to enhance the model's correction performance, particularly in high wind speed conditions. A new weighted trend-based mean squared error loss function is developed to optimize the correction process. This function integrates weighted error allocation with trend consistency constraints, effectively balancing the trade-off between minimizing errors at high wind speeds and preserving the temporal trends of wind speed distributions. The model utilizes a sparse attention mechanism that reduces computational complexity by concentrating on the most relevant interactions within the input data. Additionally, de-stationary factors (τ and Δ) are introduced to dynamically adjust attention weights, thereby enhancing the model's ability to capture non-stationary features, particularly high-speed anomalies. The experimental dataset includes historical simulations from 1961 to 2014, SSP1-2.6 scenario projections for years from 2015 to 2022, and CN05.1 gridded observations. Four representative stations including Beijing (moderate wind speed), Guaizihu and Mangya (high wind speed), and Ji'an (low wind speed) are selected for validation. Results indicate that Ns-Stationary Informer model significantly enhances the spatiotemporal characteristics of wind speed at both seasonal and decadal scales. The root mean square error (RMSE) of ensemble mean is reduced by 20%-50% compared to raw MIROC6 outputs. Specifically, for high wind speed stations (Guaizihu and Mangya), the RMSE is reduced by as much as 60% when wind speeds exceed 5 m·s-1. Monthly-scale wind speed trends corrected by Ns-Informer are improved in their agreement with observations. Additionally, the correction is particularly effective during summer and autumn, with the monthly-scale root mean square error being reduced by an average of over 25%. Further validation for 2015-2022 period under SSP1-2.6 scenario confirms the model's robustness, particularly in stabilizing corrections for high wind speed thresholds (greater than 5 m·s-1). This approach, by integrating non-stationary architecture optimization with dynamic loss function design, offers a new framework for correcting biases in climate models and enhances the accuracy of wind energy assessments in future climate change scenarios. Despite its advantages, this method is limited by its reliance on a single climate model (MIROC6) and a limited number of training ensemble members. Future work will expand this method to incorporate multi-model ensembles and long-term climate projections. The focus will be on further refining adaptive loss function and enhancing the model's generalization capabilities across various regions and scenarios.

This paper reviews the development of ensemble weather forecast and the primary techniques employed in themain ensemble prediction systems (EPSs) designed by China and other countries. Here, the emphasis is placed on theadvancements in the China Meteorological Administration (CMA) global and regional ensemble prediction systems (i.e., CMA-GEPS and CMA-REPS), with particular attention to operational technologies such as initial and modelperturbation methods and the applications of ensemble forecast. Through comparative verification with EPSs fromother leading international numerical weather prediction (NWP) centers, CMA's EPSs demonstrate forecast skillscomparable to its global counterparts. As EPSs progress to convective scales and coupled systems between sea, land,air, and ice, the paper addresses some key challenges in ensemble forecast technologies across the aspects of opera-tion, science, integration of artificial intelligence (AI), merging of weather and climate models, and challenging userrequirements. Finally, a summary of conclusions and future perspectives on ensemble forecast are provided.

This paper reviews the development of ensemble weather forecast and the primary techniques employed in the main ensemble prediction systems (EPSs) designed by China and other countries. Here, the emphasis is placed on the advancements in the China Meteorological Administration (CMA) global and regional ensemble prediction systems (i.e., CMA-GEPS and CMA-REPS), with particular attention to operational technologies such as initial and model perturbation methods and the applications of ensemble forecast. Through comparative verification with EPSs from other leading international numerical weather prediction (NWP) centers, CMA’s EPSs demonstrate forecast skills comparable to its global counterparts. As EPSs progress to convective scales and coupled systems between sea, land, air, and ice, the paper addresses some key challenges in ensemble forecast technologies across the aspects of operation, science, integration of artificial intelligence (AI), merging of weather and climate models, and challenging user requirements. Finally, a summary of conclusions and future perspectives on ensemble forecast are provided.

In June 2024,southern China(S.China)experienced record-breaking rainfall events(Fig.1c).According to the National Climate Center's June 2024 Climate Impact Assessment,since the start of the Meiyu season on June 10th,the precipitation in the Yangtze River Basin during June 10th-30th was 49.2％above the long-term average,marking the second highest rainfall in the basin since 1961(The co-authors,QP.Li and G.Xu of CMA provide this information).The heaviest rainstorms occurred during June 9th-July 2nd,with the duration and impact range exceeding that of June 1998,when also experienced extraordinary June precipitation and flooding in S.China.Associated with these catastrophic rainfall events,a large number of geological disasters occurred,such as landslides and urban flooding.The cause of this catastrophic pre-cipitation remains unidentified.It is,therefore,imperative to understand the underlying causes and make skillful predictions of such extreme hydroclimatic events for adequate monitoring,prevention,and mitigation efforts.

In order to deeply study the characteristics and laws of downlink beam in satellite in-orbit motion, this paper proposes a multi-attitude disturbing force-GNNRL (DF-GNNRL) simulation technique based on satellite downlink beam calculation. The technique first studies the satellite in-orbit motion law and the calculation parameters of downlink beam under multi-attitude adjustment, establishes the mathematical model with corresponding arithmetic power on this basis, completes the data flow analysis of multiple models in the calculation process, further designs the coding algorithm, realizes the real-time data calculation and multi-terminal storage, builds the satellite multi-attitude DFGNNRL digital twin, and finally completes the satellite multi-attitude DF-GNNRL digital twin by means of information technology. The application of this technology can provide a strong analysis basis and data support for satellite in-orbit motion and downlink beam calculation under virtual simulation conditions.

Based on long-term observational and reanalysis datasets from 1901 through 2014, this study investigates the characteristics and physical causes of the interdecadal variations in the summer precipitation over the East Asian monsoon boundary zone (EAMBZ), which is a peculiar domain defined from the perspective of the interplay between climatic systems (i.e., mid-latitude westerly and East Asian summer monsoon). Observational evidence reveals that, similarly to previous studies, the EAMBZ precipitation featured prominent interdecadal fluctuations, e.g., with dry summers during the periods preceding 1927, 1939–1945, 1968–1982, and 1998–2010 and wet summers during the periods of 1928–1938, 1946–1967, and 2011 onwards. Further analyses identify that, amongst the major interdecadal oceanic forcings (e.g., Atlantic multidecadal oscillation and Pacific decadal oscillation), the Indian Ocean basin mode (IOBM) is a significant oceanic forcing responsible for the interdecadal variations of the EAMBZ precipitation, playing an independent and critical modulation role. When the cold phase of the IOBM occurs, an anomalous cyclonic circulation is excited around the northeast corner of the tropical Indian Ocean, which further induces a north-low–south-high meridional seesaw pattern over the Northeast China–subtropical western Pacific (SWP) sector. Such seesaw pattern is conducive to the enhanced EAMBZ precipitation by linking favorable environments for the transportation of water vapor from the SWP and the convergence over the EAMBZ at interdecadal timescales. For this reason, a physical–empirical model for the EAMBZ precipitation is developed in terms of the IOBM cooling. Despite the fact that the extreme summer EAMBZ precipitation cannot be captured by this model, it can still well capture its interdecadal fluctuations and reflect their steady relationship. The key physical pathway connecting the IOBM cooling with the interdecadal variations of the summer EAMBZ precipitation is supported by the numerical results based on the large ensemble experiment and the Indian Ocean pacemaker experiment. Our findings may provide new insights into the understanding of the causes of the interdecadal variations in the summer EAMBZ precipitation, which may favor the long-term policy decision-making for the local hydrometeorological planning.

CMA-CPSv3 data for the paper "Seasonal Prediction of Regional Arctic Sea Ice Using the High-Resolution Climate Prediction System CMA-CPSv3"

在新疆天山大地形背景下,实现了中国气象局研发的高分辨率气候业务预测系统CMA-CPSv3(China Meteor-ological Administration-Climate Prediction System version 3)在天山北坡经济带的本地化应用,分别评估控制预报、传统集合平均预报以及改进后的最优概率阈值集合方法(deterministic ensemble forecast using a probabilistic threshold,DEFPT)对该区域次季节-季节降水的预测水平.评估结果表明:基于CMA-CPSv3预测系统的DEFPT方法可以提升天山北坡次季节-季节尺度1～5 mm阈值降水落区以及持续性的预测效果,优于传统集合平均预报和控制预报.从2016年7月29日—8月2日、2017年6月7-12日以及2020年7月8-12日时段发生在天山北坡的降水事件个例分析结果看,不论从降水落区、降水异常还是降水持续性,DEFPT集合预报在天山北坡西部和南部均有更好的效果,但在天山北坡东部和北部预测能力相对略低,这与该区域水汽的预报偏差增大有关.

气候预测问题是一个世界性难题,既是国际气候变化研究的前沿课题,又是国家防灾减灾、应对气候变化工作的迫切需求.气候模式是开展气候预测的最客观工具和手段.自1995年起,国家气候中心开始研发中国气象局首个海气耦合模式BCC-CM1.0,并基于该模式建立了我国第一代短期气候模式预测业务系统(CMA-CPSv1).

As one of the participants in the Subseasonal to Seasonal (S2S) Prediction Project, the China Meteorological Administration (CMA) has adopted several model versions to participate in the S2S Project. This study evaluates the models’ capability to simulate and predict the Madden-Julian Oscillation (MJO). Three versions of the Beijing Climate Center Climate System Model (BCC-CSM) are used to conduct historical simulations and re-forecast experiments (referred to as EXP1, EXP1-M, and EXP2, respectively). In simulating MJO characteristics, the newly-developed high-resolution BCC-CSM outperforms its predecessors. In terms of MJO prediction, the useful prediction skill of the MJO index is enhanced from 15 days in EXP1 to 22 days in EXP1-M, and further to 24 days in EXP2. Within the first forecast week, the better initial condition in EXP2 largely contributes to the enhancement of MJO prediction skill. However, during forecast weeks 2–3, EXP2 shows little advantage compared with EXP1-M because the increased skill at MJO initial phases 6–7 is largely offset by the degraded skill at MJO initial phases 2–3. Particularly at initial phases 2–3, EXP1-M skillfully captures the wind field and Kelvin-wave response to MJO convection, leading to the highest prediction skill of the MJO. Our results reveal that, during the participation of the CMA models in the S2S Project, both the improved model initialization and updated model physics played positive roles in improving MJO prediction. Future efforts should focus on improving the model physics to better simulate MJO convection over the Maritime Continent and further improve MJO prediction at long lead times.

Meiyu onset marks the beginning of the rainfall season in the densely populated Yangtze River Basin, whether the Meiyu initiates early or late in June, and thus has a profound effect on the several hundred million people living there. Applying a Bayesian change-point analysis to data from 1960–2014, we objectively detected an abrupt change of Meiyu onset around 2002. The Meiyu onset date averaged over 2002–2014 was 19 June, delayed by about two weeks compared to that of 1989–2001 (6 June). This decadal change is attributable to the distinct amplitude of moisture transport toward the Yangtze River Basin induced by the changes in climatological intraseasonal oscillation (CISO). The CISO emerges as the annual cycle interacts with the transient intraseasonal perturbations. The wet/dry phases of the CISO are consistent with the climatological active/break stages of the East Asian summer monsoon. In early June, the northwestward-propagating CISO convective/cyclonic anomalies over the western North Pacific (WNP) show weaker amplitude during the earlier-onset epoch compared to the delayed-onset epoch. Thus, relative to the delayed onset epoch, a quasi-barotropic anticyclonic CISO anomaly appears over the WNP in early June during the earlier-onset years. This anticyclonic anomaly was conducive to the westward extension of the WNP subtropical high, conveying warm, moist air from the tropics toward the Yangtze River Basin for the rainy season onset. Model experiments suggest that the decadal changes in WNP CISO intensity were associated with the epochal changes in large-scale background circulation and sea surface temperature over the WNP.

Skillful subseasonal prediction is crucial for meteorological disaster prevention and risk management. In this study, the subseasonal prediction skills of the new-generation coupled model of Beijing Climate Center (named as BCC-CSM2-HR) were evaluated, and a dynamical-statistical prediction model (DSPM) was developed to further improve pentad-mean precipitation predictions in China. The results show that although BCC-CSM2-HR can generally capture the climatological rain belt movement over eastern China, its skillful predictions for rainfall anomalies are basically confined within 3 pentads. By combining the dynamical model output and statistical method, a DSPM was built to capture the simultaneously coupled evolving patterns between anomalous precipitation and its atmospheric circulation predictors for each subregion of China, which was divided in terms of a cluster analysis. The 9-year independent validation shows that the prediction skills of DSPM had been significantly improved after 3 forecast pentads compared with the original model forecast. The skillful prediction can persist for a 6-pentad lead especially over the northern China and the Yangtze-Huaihe River Basin in the DSPM. As the major predictability sources of subseasonal forecasts, the Madden–Julian oscillation (MJO) and boreal summer intraseasonal oscillation (BSISO) are skillfully predicted by the BCC model for up to 23 days and 10–13 days, respectively. As a result, the improved performance of the DSPM can be largely attributed to its more realistic representation of MJO and BSISO associated circulation anomalies.

The present study investigates the interdecadal variability characteristics of South China Sea summer monsoon withdrawal（SCSSMW） and associated impacts of the Atlantic Multidecadal Oscillation（AMO） based on the NOAA-CIRES 20th reanalysis data reconstructed by the Physical Sciences Laboratory（PSL） of the National Oceanic and Atmospheric Administration（NOAA） and the Cooperative Institute for Research in Environmental Sciences（CIRES）, the Extended Reconstructed Sea Surface Temperature（ERSST） dataset from the International Comprehensive Ocean Atmosphere Data Set（ICOADS）. Numerical experiments are also implemented. The results show that the timing of SCSSMW has obvious interdecadal variability. During the late（early）SCSSMW years, there are significant cyclonic（anticyclonic） circulation anomalies and more（less） convective precipitation over the South China Sea and its nearby areas. Further studies suggest a significant positive correlation between the AMO and the interdecadal variability of SCSSMW: When the AMO is in the positive phase, SCSSMW is later and vice versa. The SST warming over the North Atlantic（i.e., AMO in the positive phase） releases more heat flux from the ocean to the atmosphere, leading to a significant increase of convective activities in the troposphere over the North Atlantic, thus triggering abnormal Rossby wave activities over the North Atlantic through sea-air interaction and enhanced convective activities. Such Rossby wave activities can affect the formation and propagation of the mid-latitude Eurasian teleconnection wave train that is closely related to the variations of atmospheric circulations over key areas in the Northeast Asia, causing positive geopotential height anomalies and significant descending motion anomalies throughout the troposphere and producing divergent motion anomalies in the lower troposphere. The energy is thus transmitted to the South China Sea and its adjacent areas with the anomalous divergent wind flows, producing convergent and ascending motion anomalies. The cyclonic circulation anomaly over the South China Sea further enhances, leading to later SCSSMW. Roughly opposite mechanism works during the negative phase of AMO, leading to earlier SCSSMW.

The changes in near-surface wind speed (NWS) have a crucial influence on the wind power industry, and previous studies have indicated that NWS on global and China has declined continuously for decades under global warming. However, recently, the decreasing trend of global NWS has slowed down and even showed a recovery trend. Using the observation data of 831 weather stations of the China Meteorological Administration and the Japanese 55-year reanalysis data from 1970 to 2019, NWS changes in eastern China were analyzed and the possible influencing factors were discussed. Results show that winter NWS presented a decreasing trend from −0.29 m s−1 per decade (p < 0.001) in 1970–1989 to −0.05 m s−1 per decade (p < 0.01) in 1990–2019. Moreover, NWS exhibited a significant upward trend of 0.18 m s−1 per decade (p < 0.1) in 2011–2019, resulting in a 19.6% per decade recovery of the wind power generation. A possible cause is asymmetric changes of the sea level pressure and near-surface air temperature differences between the mid-high latitudes (40°–60°N, 80°–120°E) and low latitudes (20°–40°N, 110°–140°E) altered the horizontal air pressure gradient. Furthermore, NWS changes were closely associated with the large-scale ocean-atmosphere circulations (LOACs). NWS at 77.4% of the stations in eastern China shows significant correlation (p < 0.05) with the East Asian winter monsoon index, besides, the inter/multidecadal variability of NWS was considerably correlated to four LOACs, including Arctic oscillation (AO), North Atlantic oscillation (NAO), Pacific decadal oscillation (PDO), and El Niño–Southern Oscillation (ENSO). The time-series reconstructed by a multiple linear regression model based on above five LOACs matches well with the NWS. Interannual variability of NWS were significantly correlated to AO (−0.45, p < 0.01) and NAO (−0.28, p < 0.05), while the correlation between NWS and ENSO was weak.

Based on the 20 years of ensemble hindcast data, we evaluated the performance of the new version climate prediction system developed by the China Meteorological Administration (CMA CPSv3) on the Asian summer monsoon (ASM) seasonal prediction in this study. Many major features of the ASM are well predicted by CPSv3, including the intensity and location of the heavy precipitation centers, large-scale monsoon circulations, monsoon onset, and the interannual variation of dynamical monsoon indices. The model captures realistically interannual variability of the summer western North Pacific subtropical high (WNPSH) and is highly skillful for the WNPSH index. Compared with its predecessor, the prediction skill of summer precipitation over Asia in CPSv3 is obviously improved, especially over eastern China. The improvement mainly benefits from skillful predictions of the tropical Pacific Ocean and tropical Indian Ocean sea surface temperatures and ocean–atmosphere coupling associated with them.

Based on multiple long‐term observational and reanalysis datasets, this study investigated the characteristics and physical mechanisms of the interdecadal variations in late spring (i.e., May) precipitation (LSP) over the southeastern extension of the Tibetan Plateau (SETP) since 1900. It was revealed that by and large, LSP over the SETP experienced interdecadal decrease during the period preceding 1927, 1962–1988, and 2004 onwards, but saw an increase during the periods of 1928–1961 and 1989–2003. The atmospheric circulations responsible for interdecadal variations in LSP over the SETP were also analysed. These analyses identified significant synergistic impacts of decreased mid‐latitude upstream westerlies and increased low‐latitude monsoonal southerlies over the Central North Bay of Bengal (CNBOB) on interdecadal variations in precipitation, suggesting striking interactions between extratropical eastward cold air and tropical northward warm/humid air. Further observational and modelling evidence suggested that Atlantic Multidecadal Oscillation (AMO) was likely to be a salient oceanic driver for the interdecadal synergy between upstream westerlies and CNBOB monsoonal southerlies. The elevated sea surface temperature anomalies associated with the warm phase of the AMO could spark favourable local atmospheric anomalies, forcing an upper‐tropospheric, planetary‐scale teleconnection emanating from the east of the North Atlantic sector, which may serve as an effective bridge linking the remote AMO signal and the synergy between westerlies and monsoonal southerlies around the SETP on interdecadal timescales. Our findings provided new insights into the understanding of the synergistic roles of westerlies and monsoons in the modulation of interdecadal LSP over the SETP, prior to the peak Asian summer monsoon season.