Madden–Julian Oscillation Prediction Research Articles

How Long Can the MJO be Predicted During the Combined Phases of ENSO and QBO?

AbstractThe predictability limit of the Madden‐Julian Oscillation (MJO) in the El Niño and La Niña boreal winters (November‐February) is investigated using observational data and historical run outputs of CMIP models. The MJO predictability is computed by the nonlinear local Lyapunov exponents approach for various MJO indices obtained from bandpass‐filtered (30–80 days) outgoing longwave radiation, 850‐hPa zonal wind, and 200‐hPa zonal wind data. The result shows that the MJO predictability during La Niña winters is enhanced by up to 8 days compared to El Niño winters. Enhanced convection of the MJO phases 2–4 with persistence propagation during La Niña winters leads to higher predictability, as compared with those for El Niño winters. The highest (lowest) predictability of the MJO shows 39 (31) days during La Niña/EQBO (El Niño/EQBO) winters. The longer persistence of the MJO during La Niña/EQBO winters leads to higher predictability.

S2S Prediction in GFDL SPEAR: MJO Diversity and Teleconnections

Abstract A subseasonal-to-seasonal (S2S) prediction system was recently developed using the GFDL Seamless System for Prediction and Earth System Research (SPEAR) global coupled model. Based on 20-yr hindcast results (2000–19), the boreal wintertime (November–April) Madden–Julian oscillation (MJO) prediction skill is revealed to reach 30 days measured before the anomaly correlation coefficient of the real-time multivariate (RMM) index drops to 0.5. However, when the MJO is partitioned into four distinct propagation patterns, the prediction range extends to 38, 31, and 31 days for the fast-propagating, slow-propagating, and jumping MJO patterns, respectively, but falls to 23 days for the standing MJO. A further improvement of MJO prediction requires attention to the standing MJO given its large gap with its potential predictability (38 days). The slow-propagating MJO detours southward when traversing the Maritime Continent (MC), and confronts the MC prediction barrier in the model, while the fast-propagating MJO moves across the central MC without this prediction barrier. The MJO diversity is modulated by stratospheric quasi-biennial oscillation (QBO): the standing (slow-propagating) MJO coincides with significant westerly (easterly) phases of QBO, partially explaining the contrasting MJO prediction skill between these two QBO phases. The SPEAR model shows its capability, beyond the propagation, in predicting their initiation for different types of MJO along with discrete precursory convection anomalies. The SPEAR model skillfully predicts the observed distinct teleconnections over the North Pacific and North America related to the standing, jumping, and fast-propagating MJO, but not the slow-propagating MJO. These findings highlight the complexities and challenges of incorporating MJO prediction into the operational prediction of meteorological variables.

Climate Process Team: Improvement of Ocean Component of NOAA Climate Forecast System Relevant to Madden‐Julian Oscillation Simulations

AbstractGiven the increasing attention in forecasting weather and climate on the subseasonal time scale in recent years, National Oceanic and Atmospheric Administration (NOAA) announced to support Climate Process Teams (CPTs) which aim to improve the Madden‐Julian Oscillation (MJO) prediction by NOAA’s global forecasting models. Our team supported by this CPT program focuses primarily on the improvement of upper ocean mixing parameterization and air‐sea fluxes in the NOAA Climate Forecast System (CFS). Major improvement includes the increase of the vertical resolution in the upper ocean and the implementation of General Ocean Turbulence Model (GOTM) in CFS. In addition to existing mixing schemes in GOTM, a newly developed scheme based on observations in the tropical ocean, with further modifications, has been included. A better performance of ocean component is demonstrated through one‐dimensional ocean model and ocean general circulation model simulations validated by the comparison with in‐situ observations. These include a large sea surface temperature (SST) diurnal cycle during the MJO suppressed phase, intraseasonal SST variations associated with the MJO, ocean response to atmospheric cold pools, and deep cycle turbulence. Impact of the high‐vertical resolution of ocean component on CFS simulation of MJO‐associated ocean temperature variations is evident. Also, the magnitude of SST changes caused by high‐resolution ocean component is sufficient to influence the skill of MJO prediction by CFS.

Influence of the Madden–Julian Oscillation on the Arctic Oscillation Prediction in S2S Operational Models

The connections between the Madden–Julian Oscillation (MJO) and the Arctic Oscillation (AO) are examined in both observations and model forecasts. In the observations, the time-lag composites are carried out for AO indices and anomalies of 1,000-hPa geopotential height after an active or inactive initial MJO. The results show that when the AO is in its positive (negative) phase at the initial time, the AO activity is generally enhanced (weakened) after an active MJO. Reforecast data of the 11 operational global circulation models from the Sub-seasonal to Seasonal (S2S) Prediction Project are further used to examine the relationship between MJO activity and AO prediction. When the AO is in its positive phase on the initial day of the S2S prediction, an initial active MJO can generally improve the AO prediction skill in most of the models. This is consistent with results found in the observations that a leading MJO can enhance the AO activity. However, when the AO is in its negative phase, the relationship between the MJO and AO prediction is not consistent among the 11 models. Only a few S2S models provide results that agree with the observations. Furthermore, the S2S prediction skill of the AO is examined in different MJO phases. There is a significantly positive relationship between the MJO-related AO activity and the AO prediction skill. When the AO activity is strong (weak) in an MJO phase, including the inactive MJO, the models tend to have a high (low) AO prediction skill. For example, no matter what phase the initial AO is in, the AO prediction skill is generally high in MJO phase 7, in which the AO activity is generally strong. Thus, the MJO is an important predictability source for the AO forecast in the S2S models.

The Role of Atmospheric Drivers in a Sudden Transition of California Precipitation in the 2012/13 Winter

AbstractCalifornia experienced a sharp transition from wet to historically dry conditions during the 2012/13 winter. Such transitions in the precipitation regime have strong consequences for water management, but predicting them at the seasonal time scale remains very challenging as little is known about what drives them. The 2012/13 winter was characterized by a neutral El Niño Southern Oscillation, strong Madden‐Julian Oscillation (MJO) activity, and sudden stratospheric warming. This study examines the potential influence of these different drivers in atmospheric global climate model experiments with prescribed sea surface temperature (SST) and sea ice concentration and constrained tropical and/or Arctic variability. Our model results are compared to reforecasts from the North American Multi‐Model Ensemble (NMME) and Subseasonal Experiment (SubX)/Subseasonal to Seasonal (S2S) projects to determine which important processes may have been missed by the seasonal prediction systems. Our simulations suggest that the tropical warm‐west, cool‐east SST anomaly played an active role in the wet conditions of November–December 2012 through a typical tropical‐extratropical teleconnection. The tropical SST anomalies were not accurately predicted by the NMME models, and none of them predicted the wet anomalies in November–December. In contrast, the sudden transition toward dry conditions in January is found to be independent of tropical SST variability, and rather influenced by strong MJO activity over the western Pacific. The SubX/S2S models with a better MJO prediction tend to predict the dry conditions in January better. Our study highlights how intraseasonal processes superimpose on more persistent seasonal anomalies and induce regime shifts such as the wet‐to‐dry transition of the 2012/13 winter.

An exploration of the connection between quasi-biennial oscillation and Madden-Julian oscillation

Recent studies have shown arguments about the connection between stratospheric quasi‐biennial oscillation (QBO) and activities of Madden‐Julian oscillation (MJO) during boreal winter on interannual timescale, especially for the question of which aspects of MJO, amplitude or occurrence frequency, can really be modulated by QBO. This study re-examines the interannual variability of the seasonally averaged amplitude and the occurrence of both ‘active’ (amplitude > 1.0) and ‘inactive’ (amplitude < 1.0) MJO days in boreal winter December–January–February (DJF), and their relation to QBO variation. Significant correlations between the QBO index and the indicators of MJO activity reveal that wintertime amplitude of ‘active’ and ‘inactive’ MJO days are typically larger in QBO easterly phase (EQBO) than in QBO westerly phase (WQBO). More importantly, we can also expect more ‘active’ MJO days and simultaneously less ‘inactive’ MJO days in EQBO winter as a consequence of more days in winter shifting from ‘inactive’ to ‘active’ due to larger MJO amplitudes. The significantly positive (negative) correlation between the time series of averaged amplitude and occurrence of ‘active’ (‘inactive’) MJO days can as well be interpreted as evidence that QBO is closely connected to the occurrence of MJO days via modulating MJO amplitudes. These results update our current understanding of the QBO-MJO connection, which is helpful to advance the MJO prediction.

Seasonality in Prediction Skill of the Madden‐Julian Oscillation and Associated Dynamics in Version 2 of NASA's GEOS‐S2S Forecast System

AbstractThe prediction skill of the Madden‐Julian Oscillation (MJO) in Version 2 of NASA's Global Earth Observing System Subseasonal to Seasonal (GEOS‐S2S) forecast system is investigated for winter and summer focusing on moistening‐related processes crucial for eastward propagating MJO activity. It is found that the annual bivariate correlation of the Real‐time Multivariate MJO time series between prediction and observation is ∼0.70, ∼0.57, and ∼0.50 at 20‐, 25‐, and 30‐day forecast leads. Correlation at long‐leads (&gt;30 days) is noticeably higher for boreal summer initial conditions (June‐September [JJAS]), with correlations remaining above 0.5 at 35–40 days leads. Correlations are lower for boreal winter initial conditions from January through March (JFM), dropping to ∼0.5 at 25‐day lead, still comparable to the skills in the other reliable S2S forecast systems.The predicted eastward MJO propagation across the Indo‐western Pacific sector is well captured in JJAS, but is slower than observed in JFM. Investigations of the moisture field and advection and moisture sink, moist static energy (MSE) budget, and tropical circulation/pressure responses to the MJO convective heating reveal that, in JFM, those responses and moistening processes, especially the vertical MSE advection, are underestimated over and to the east of the Maritime Continent when the MJO anomaly approaches from the west. In contrast, those processes are well represented in JJAS, although moistening is overestimated due to large surface evaporation. This study suggests that improvement of the moistening tendency over that region in the boreal winter could contribute to further increases in MJO prediction skill of the GEOS‐S2S system.

NAO Influence on the MJO and its Prediction Skill in the Subseasonal-to-Seasonal Prediction Models

AbstractBased on the database of the Subseasonal to Seasonal (S2S) Prediction project of the World Weather Research Programme (WWRP) / World Climate Research Programme (WCRP), the influence of the North Atlantic Oscillation (NAO) on the Madden- Julian Oscillation (MJO) and its forecast skill is investigated. It is found that most models can capture the MJO phase changes following positive and negative NAO events. About 20 days after initialized with a positive (negative) NAO, the forecast MJO appears more frequently in phase 7 (3), which corresponds to reduced (enhanced) convection in the tropical Indian Ocean and enhanced (suppressed) convection in the western Pacific. In most S2S models the MJO prediction skill is dependent on the NAO amplitude and phase in the initial condition. A strong NAO leads to a better MJO forecast skill than a weak NAO. The MJO skill tends to be higher when the forecast starts from a negative NAO than a positive NAO. These results indicates that there is a strong Northern extratropical influence on the MJO and its forecast skill. It is important for numerical models to better represent the NAO influence to improve the simulation and prediction of the MJO.

Assessing the role of air-sea coupling in predicting Madden-Julian Oscillation with an atmosphere-ocean coupled model

AbstractThe Madden-Julian Oscillation (MJO) provides an important source of sub-seasonal to seasonal (S2S) predictability. Improved MJO prediction can be beneficial to S2S prediction of global climate and associated weather extremes. In this study, hindcasts based on an atmosphere-ocean coupled general circulation model (CGCM) are compared to those based on atmosphere general circulation models (AGCMs) to investigate influences of air-sea interactions on MJO prediction. Our results suggest that MJO prediction skill can be extended about one week longer in the CGCM hindcasts than AGCM-only experiments, particularly for boreal winter predictions.Further analysis suggests that improved MJO prediction in the CGCM is closely associated with improved representation of moistening processes. Compared to the AGCM experiments, the CGCM better predicts the boundary-layer moisture preconditioning to the east of MJO convection, which is generally considered crucial for triggering MJO deep convection. Meanwhile, the widely extended east-west asymmetric structure in free-tropospheric moisture tendency anomalies relative to the MJO convection center as seen in the observations is also well predicted in the CGCM. Improved prediction of MJO moisture processes in CGCM is closely associated with better representation of the zonal scale of MJO circulation and stronger Kelvin waves to the east of MJO convection, both of which have been recently suggested conductive for MJO eastward propagation. The above improvements by including air-sea coupling could be largely attributed to the realistic MJO-induced SST fluctuations through the convection-SST feedback. This study confirms a critical role of atmosphere-ocean coupling for the improvement of MJO prediction.

The impact of coupled data assimilation on Madden-Julian Oscillation predictability initialized from coupled satellite-era reanalysis

AbstractThis study investigates the impact of coupled initialization on the extended-range prediction of the Madden-Julian Oscillation (MJO). A set of reforecasts using combinations of the oceanic and atmospheric initial conditions produced with coupled and uncoupled data assimilation (DA) are conducted to evaluate the impact of coupling in the different domains, from the perspective of MJO forecasts. The coupled initial conditions are provided by CERA-SAT pilot coupled reanalysis for the satellite era recently produced by ECMWF. We focus on the prediction skill of the MJO using the Real-time Outgoing Long-wave Radiation (OLR) MJO index in a series of re-forecasts. The impact of atmospheric initial conditions produced by coupled DA shows slight benefit for the MJO prediction. However, compared with the operational ocean reanalysis, the ocean initial conditions created by CERA-SAT degrade the MJO prediction skill during the first 2-3 weeks of the re-forecast by 1.5% to 5.8%. A moist static energy budget analysis revealed that the underestimation of 0.2 K sea surface temperature, 1.4 W m-2 top of atmosphere downward longwave radiation, and 3.8 W m-2 latent heat flux over the Maritime Continent lead to small but statistically significant degradation of the MJO forecast skill. The results demonstrate that the MJO is sensitive to ocean initial conditions, and illustrate the value of the extended range MJO prediction for evaluating the quality of coupled data assimilation, and suggest that future efforts on coupled data assimilation pay special attention to the balance of air-sea interaction processes over the warm pool area, in terms of modeling, observational needs and system.

Deep learning for bias correction of MJO prediction

Producing accurate weather prediction beyond two weeks is an urgent challenge due to its ever-increasing socioeconomic value. The Madden-Julian Oscillation (MJO), a planetary-scale tropical convective system, serves as a primary source of global subseasonal (i.e., targeting three to four weeks) predictability. During the past decades, operational forecasting systems have improved substantially, while the MJO prediction skill has not yet reached its potential predictability, partly due to the systematic errors caused by imperfect numerical models. Here, to improve the MJO prediction skill, we blend the state-of-the-art dynamical forecasts and observations with a Deep Learning bias correction method. With Deep Learning bias correction, multi-model forecast errors in MJO amplitude and phase averaged over four weeks are significantly reduced by about 90% and 77%, respectively. Most models show the greatest improvement for MJO events starting from the Indian Ocean and crossing the Maritime Continent.

Potential predictability of the MJO during easterly and westerly phases of the QBO

The potential predictability of the Madden–Julian Oscillation (MJO) in boreal winter (November–February) is investigated using observational data for the period of 1979–2016. For various MJO indices, nonlinear local Lyapunov exponents are computed to quantify the MJO predictability under the easterly and westerly phases of the Quasi-Biennial Oscillation (easterly: EQBO and westerly: WQBO). All MJO indices exhibit higher predictability during EQBO winters than during WQBO winters. Excluding strong ENSO years from EQBO and WQBO winters has a limited impact on MJO predictability. The highest potential predictability of 43 days during EQBO winters and 37 days during WQBO winters is found for the MJO index obtained from bandpass-filtered (30–80 days) outgoing longwave radiation and wind data. In contrast, the potential predictability of the MJO from the real-time multivariate MJO index is 21 days during EQBO winters and 13 days during WQBO winters. The longer persistence and less disorganization of the MJO during the EQBO winters lead to the higher predictability for EQBO winters, as compared with that for WQBO winters.

Improving the MJO Forecast of S2S Operation Models by Correcting Their Biases in Linear Dynamics

AbstractThe operational dynamic subseasonal to seasonal (S2S) models for Madden‐Julian oscillation (MJO) forecasting mostly still suffer from systematic errors in capturing the MJO's key dynamic features, such as its growth rate and propagation speed. By deriving the linear dynamic operators using the linear inverse modeling (LIM) approach, we propose a method to partly correct the errors in MJO linear dynamic operators to improve the MJO predictions of three operational dynamic S2S models. Correcting the deficiencies of the too‐fast decay rates and the unrealistic propagating phase speeds lead to MJO prediction skills being extended by approximately 2–4 days. The improvements are more significant for the models with larger biases in MJO amplitude and propagation. This approach in principle may be extendable to predictions of other types of climate variability such as ENSO on one hand, and possible inclusions of nonlinear dynamics effects on the other hand.

Assessing Predictive Potential Associated with the MJO during the Boreal Winter

AbstractIn this paper, the question of potential predictability in meteorological variables associated with skillful prediction of the Madden–Julian oscillation (MJO) during boreal winter is analyzed. The analysis is motivated by the fact that dynamical prediction systems are now capable of predicting MJO up to 30 days or earlier (measured in terms of anomaly correlation for RMM indices). Translating recent gains in MJO prediction skill and relating them back to potential for predicting meteorological variables—for example, precipitation and surface temperature—is not straightforward because of a chain of steps that go into the computation and evaluation of RMM indices. This paper assesses potential predictability in meteorological variables that could be attributed to skillful prediction of the MJO. The analysis is based on the observational data alone and assesses the upper limit of MJO-associated predictability that could be achieved.

MJO Wind Energy and Prediction of El Niño

AbstractThis study applies the energetic framework developed to quantify the interaction between the Madden‐Julian Oscillation (MJO) and El Niño–Southern Oscillation (ENSO) to El Niño forecasting. The MJO wind power, measured by the covariability of MJO‐related wind stress and oceanic Kelvin wave activity, is combined with the sea surface temperature (SST) anomalies, and two new indices are proposed. The MJO‐Kelvin wave‐ENSO (MaKE) index is proposed as an ENSO predictor and is shown to slightly outperform Niño 3.4 when applied to observational data sets and to greatly outperform Niño 3.4 when applied to CFSv2 reforecasts of the years 1980–2014. The MJO‐Kelvin wave Influence (MaKI) index is proposed to predict MJO influence on developing El Niño events. This index performs reasonably well when applied to observations. The forecast skill of MaKI in the CFSv2 reforecasts suggests that this model does not predict the observed MJO‐ENSO relationship as measured by this index.

MJO Prediction Skill Using IITM Extended Range Prediction System and Comparison with ECMWF S2S

Eastward propagating Madden–Julian Oscillation (MJO) is a dominant mode of the intraseasonal variability and hence a potential source of intraseasonal predictability. Therefore, advancing MJO prediction using state-of-the-art dynamical model is of utmost importance for improving intraseasonal prediction. The prediction skill and predictability of MJO are assessed using 44 members ensemble hindcast (16 years data; 2001–2016) of CFSv2 based extended range prediction (ERP) system of IITM as well as 10 member ensemble hindcast (16 years data; 2001–2016) of ECMWF S2S dataset. The MJO is diagnosed using a newly developed Extended Empirical Orthogonal Function (EEOF) analysis. Near equatorial (15° S–15° N) model anomaly fields are projected onto the leading pair of observed eigen modes. The leading pair of observed eigen modes are obtained based on the EEOF analysis of the combined field of zonal wind at 200 hPa (U200), zonal wind at 850 hPa (U850) and velocity potential at 200 hPa (chi200). Model forecasted principal components (PCs) are quantitatively compared with observed PCs using bivariate correlation coefficient and root mean square error (RMSE). We find that MJO could be predicted up to around 22 days (around 31 days) for IITM ERP system (ECMWF S2S dataset) as measured by anomaly correlation coefficient remains larger than 0.5 and RMSE remains lower than 1.4. This prediction skill is quite low compared to potential predictability, which is estimated as more than 40 days both for IITM-ERP and ECMWF system. MJO prediction skill varies with initial MJO phase, particularly at the longer lead. This variation is more significant for the ECMWF system. Model (both for IITM-ERP and ECMWF) predicted amplitude drops at a faster rate and phase propagation speed for almost all initial phase is slower and amplitude is weaker compared to the observation. It could be concluded that even the state-of-the-art models [IITM-ERP (basically NCEP CFSv2) and ECMWF] are also not free from systematic errors/biases. Hence, there is an enormous space for improving MJO prediction skill by reducing these errors/biases in the dynamical model and error in the initial condition.

Effects of high-frequency surface wind on the intraseasonal SST associated with the Madden-Julian oscillation

The effect of high-frequency (< 20 days) wind on the intraseasonal sea surface temperature (SST) anomaly associated with the Madden-Julian oscillation (MJO) is examined by diagnosing reanalysis and outputs from a set of oceanic general circulation model (OGCM) experiments. Warm SST anomaly (SSTA) ahead of MJO convective center induces anomalous boundary-layer convergence, favoring the eastward propagation of the MJO. To understand the key physical processes contributing to the warm SSTA, the mixed-layer heat budget equation is diagnosed. The time change of SSTA ($$\partial \left\langle T \right\rangle /\partial t$$) mostly comes from shortwave radiative heating, while latent heat flux (LHF) plays the secondary role. Due to the strong nonlinearity of LHF, the high-frequency (< 20 days) wind may affect the intraseasonal LHF variability via interacting with the background state, resulting in changes in intraseasonal SSTA. Our diagnosis shows that the upscale feedback associated with high-frequency wind variability accounts for around 23% of the intraseasonal LHF in the intraseasonal SST warming region, supporting the growth of $$\partial \left\langle T \right\rangle /\partial t$$. Sensitivity experiments are then designed using an OGCM that simulates the upper-ocean temperature well, to verify the high-frequency wind effect on the intraseasonal SST variability. Once the high-frequency component of surface winds is removed in the model integration, the amplitudes of intraseasonal LHF and $$\partial \left\langle T \right\rangle /\partial t$$ are decreased, leading to reduced SSTA. The modeling results confirm the positive role of high-frequency wind in supporting the tropical intraseasonal SST variation. The findings of this study suggest that an accurate representation of high-frequency disturbances and their interaction with other components are crucial for MJO simulation and prediction.

Optimal error analysis of MJO prediction associated with uncertainties in sea surface temperature over Indian Ocean

In this study, the predictability of the Madden–Julian Oscillation (MJO) is investigated using the coupled Community Earth System Model (CESM) and the climatically relevant singular vector (CSV) method. The CSV method is an ensemble-based strategy to calculate the optimal growth of the initial error on the climate scale. We focus on the CSV analysis of MJO initialized at phase II, facilitating the investigation of the effect of the initial errors of the sea surface temperature (SST) in the Indian Ocean on it. Six different MJO events are chosen as the study cases to ensure the robustness of the results. The results indicate that for all the study cases, the optimal perturbation structure of the SST, denoted by the leading mode of the singular vectors (SVs), is a meridional dipole-like pattern between the Bay of Bengal and the southern central Indian Ocean. The MJO signal tends to be more converged and significant in the Eastern Hemisphere while the model is perturbed by leading SV. The moist static energy analysis results indicate that the eastward propagation is much more evident in the terms of vertical advection and radiation flux than others. Therefore, the SV perturbation can strengthen and converge the MJO signal mostly by increasing the vertical advection of the moist static energy. Further, the sensitivity studies indicate that the structure of the leading SV is not sensitive to the initial states, which suggests that we might not need to calculate SVs for each initial time in constructing the ensemble prediction, significantly saving computational time in the operational forecast systems.

The predictability limit of the amplitude and phase of the Madden‐Julian oscillation

AbstractThe Madden–Julian Oscillation (MJO) is characterized by slowly eastward‐propagating precipitation and circulation anomalies with time scales of about 30–80 days. Both the phase and amplitude of the MJO fluctuate with time as it propagates eastward. Despite recent progress in understanding the predictability limit of the MJO as a whole, little is known of the difference in the predictability limits of its amplitude and phase. This paper investigates these differences using the nonlinear local Lyapunov exponent approach, which provides an estimate of atmospheric predictability based on observational data. The predictability limit of the phase of the MJO is determined as ~32 days, which is higher than that of its amplitude (about 16 days). In state‐of‐the‐art operational forecast models, the phase of the MJO is also found to have a much better forecast skill than does its amplitude. The relatively low limit of the predictability of the amplitude will pose a challenge to MJO prediction.

Effects of Moisture Initialization on MJO and its Teleconnection Prediction in BCC Subseasonal Coupled Model

AbstractIt is well recognized that moisture is of great importance in the representation of the Madden‐Julian Oscillation (MJO). In this study, a moisture initialization scheme is developed using the Beijing Climate Center (BCC) subseasonal operational coupled model, and its effects on MJO predictions are investigated. Three sets of hindcast experiments are conducted: a reference experiment without moisture initialization (REF) and two sensitivity experiments with moderate and strong moisture nudging schemes. We show that the prediction skill of the real‐time multivariate MJO index is significantly improved in the two sensitivity experiments, where the skillful prediction can reach up to 20 days using the moderate nudging scheme. Larger improvements can be achieved when MJO was initiated from Phases 2–3 and 5–7 or during La Niña episodes, implying that the MJO prediction skill is more sensitive to moisture conditions in active convection regions and backgrounds. The moisture budget analysis indicated that the MJO prediction improvement using the moisture initialization schemes comes from the realistic representation of vertical moisture structure and vertical moisture advection to the east of the MJO convection in the planetary boundary layer. The improved MJO prediction is also demonstrated to enable a more reliable subseasonal prediction of extratropical circulation and precipitation through a more realistic description of MJO‐related teleconnections. In addition, the ensemble mean of the three schemes can extend the MJO prediction skill to more than 22 days, showing the potential benefits of ensemble prediction of multiinitialization schemes.