Why Indian Summer Monsoon fails despite La Niña conditions: role of Madden–Julian Oscillation phase persistence

Abstract

This study examines the combined influence of El Niño–Southern Oscillation (ENSO) and Madden–Julian Oscillation (MJO) characteristics on the interannual variability of the Indian Summer Monsoon Rainfall (ISMR), with a specific focus on La Niña years. Persistent equatorial Pacific cold tongue SST anomalies during the June to September (JJAS) season establish a favorable large-scale background for monsoon enhancement. However, ISMR exhibits substantial variability across La Niña years, ranging from excess to deficit rainfall, indicating that ENSO forcing alone is insufficient to explain monsoon outcomes. To address this, the role of intraseasonal variability associated with the MJO was investigated through phase frequency, persistence, and propagation characteristics during the June to September (JJAS) season. Results show that MJO phases 2–4 occur marginally more frequently than phases6–8, but phase persistence exhibits a stronger relationship with ISMR. Enhanced persistence in phases 2–4 is associated with excess rainfall, whereas prolonged residence in phases 6–7 shows a statistically significant negative correlation with ISMR. Year-wise analysis further demonstrates that monsoon deficits arise not only from the presence of suppressed phases but also from the absence of active phases. To further elucidate the mechanisms underlying these statistical relationships, composite analyses of contrasting monsoon conditions of wet and dry years were examined using detailed dynamical diagnostics. The analysis reveals a dynamically coherent and monsoon-supportive circulation structure during wet La Niña years, whereas during dry La Niña years it shows weakened ascent, eastward displacement of convective activity, and reduced dynamical support over the Indian region. In wet La Niña years, the MJO signal persisted in Phases 2–4, supporting moisture convergence over the Indian landmass, leading to higher rainfall. In dry La Niña years, the MJO signal persisted in Phases 6–7, favoring moisture divergence over the region and contributing to lower rainfall. These contrasts confirm that the seasonal realization of La Niña forcing depends critically on MJO phase persistence and associated large-scale circulation adjustments.

1 Introduction

The Indian Summer Monsoon Rainfall, often abbreviated as ISMR, is a critical component of India’s climate system, encompassing the rainfall received during the summer monsoon season, typically spanning from June to September (JJAS) (Rajeevan et al., 2012). Its impact reverberates across various sectors of the economy, directly influencing agricultural productivity, water sustainability, hydropower generation, and public health, as described in multiple studies, such as in Gadgil and Gadgil (2006), Athira et al. (2023), Mishra et al. (2024), Mondal et al. (2025). Traditionally, the monsoon has been characterized as the seasonal reversal of prevailing wind, bringing moisture-laden air toward the Indian mainland and precipitating rainfall over the region during the summer months (Webster et al., 1998). However, it is increasingly recognized that monsoon dynamics extend beyond mere wind patterns and are intricately linked to large-scale ocean–atmosphere interactions, with one of the most prominent drivers being the El Niño Southern Oscillation (ENSO) (Fasullo and Webster, 2003). With El Niño events often linked to monsoon droughts and La Niña events typically associated with wetter conditions (Mooley and Parthasarathy, 1984; Kulkarni, 1997; Preethi et al., 2017). However, this relationship is not always straightforward, as some El Niño years fail to produce droughts (Coelho and Goddard, 2009; Ratna et al., 2024) and certain La Niña years do not enhance rainfall as expected (Chakraborty, 2018; Mujumdar et al., 2020). A recent study by Gadgil et al. (2023) has identified “rogue La Niña” years during which ISMR was below-normal, emphasizing that ENSO alone is insufficient to explain monsoon variability. These inconsistencies have led to a growing interest in additional modulators, regional sea surface temperature (SST) patterns and convective processes, particularly the Indian Ocean Dipole (IOD), and the Madden-Jullian Oscillation (MJO; Madden and Julian, 1971), which can modify the ENSO-ISMR linkage (Cherchi et al., 2021).

The influence of the IOD on ISMR has been extensively studied, especially during extreme positive IOD events, which can enhance monsoon rainfall by strengthening low-level convergence over India (Ratna et al., 2021). However, the interaction between ENSO and IOD remains complex. A study done by Mohanty and Padhi (2022), suggests that India’s rainfall is more during La Niña and negative IOD events together compared to only La Niña and only positive/negative IOD events. Prolonged episodes like triple-dip La Niña are often associated with normal to above-normal ISMR (Musale et al., 2024). Additionally, the phase transition of ENSO, such as from El Niño to La Niña, are associated with enhanced ISMR than a persistent La Niña (Zhu et al., 2020; Sharma et al., 2024).

While ENSO and IOD are well-established modulators of the ISMR, the MJO represents another critical yet less understood factor influencing monsoon variability, particularly its onset and active-break cycles (Taraphdar et al., 2018; Ratna et al., 2024). Recent studies suggest that MJO propagation has undergone notable changes, with an increase in standing oscillations and a slowdown in eastward propagation, particularly over the Indo-Pacific warm pool in recent decades (Wang and Wang, 2023; Zhang and Han, 2020; Kim et al., 2025). These changes are linked to a westward shift in Walker circulation and a reduction in fast-propagating events, which could alter MJO interactions with ENSO and, consequently, ISMR.

The 1999 climate shift, associated with a La Niña–like mean-state change, has been linked to an increased frequency of standing oscillations and slow-propagating MJO events, alongside a weakening of MJO amplitude over the central Pacific (Wang and Wang, 2023; Kim, 2024). This shift has strengthened convective activity over the western Pacific while hindering MJO propagation into the central and eastern Pacific. Given that MJO-ENSO interactions play a crucial role in modulating ISMR, these changes may have significant implications for monsoon variability. Studies show that during El Niño years, MJO activity is largely confined to the Indian Ocean, increasing the likelihood of high-intensity rainfall events over South Peninsular India during the northeast monsoon (Sreekala et al., 2018). Conversely, during strong El Niño conditions, MJO activity shifts toward the western Pacific, contributing to suppressed rainfall over India due to anomalous subsidence and weakened south-westerly winds (Bhuyan et al., 2018; Ratna et al., 2021).

Despite extensive research on ENSO–MJO interactions and their influence on the ISMR, important gaps remain in understanding why La Niña does not consistently lead to enhanced monsoon rainfall. Most previous studies have primarily focused on the frequency or occurrence of MJO phases, with relatively limited attention to the role of phase persistence and its dynamical implications. The present study advances this understanding by explicitly quantifying MJO phase-wise persistence during La Niña years and examining its relationship with ISMR variability. In addition, this work provides a comprehensive dynamical interpretation by linking phase-dependent persistence to large-scale circulation features, including Walker circulation, low-level monsoon flow, and equatorial wave responses (Kelvin and equatorial Rossby waves). By combining statistical analysis with composite-based dynamical diagnostics for wet and dry La Niña conditions, this study offers a unified framework that connects intraseasonal variability with interannual monsoon outcomes. This integrated perspective highlights that the persistence and organization of MJO phases, rather than their mere frequency, play a decisive role in determining whether La Niña conditions result in excess or deficit monsoon rainfall.

2 Methodology

2.1 Identification of La Niña years

La Niña years were identified using the ERSSTv5 (Huang et al., 2017) monthly SST dataset. Seasonal mean SST anomalies for the JJAS period were calculated over the Niño3.4 region (5°N–5°S, 120°W–170°W), relative to the 1991–2020 climatology and the SST time series was linearly detrended to remove long-term warming. La Niña years were defined as those with detrended JJAS seasonal SST anomalies ≤ − 0.5 °C, while anomalies between −0.5 °C and +0.5 °C were classified as neutral. A total of 20 La Niña events were identified during 1951–2022; however, 13 events (1974–2022) were used for analysis based on MJO data availability.

2.2 Phase-wise frequency and normalization of MJO activity

Intra-seasonal variability associated with the MJO was analysed using the Real-time Multivariate MJO (RMM1 and RMM2) indices (Wheeler and Hendon, 2004). Active MJO days (amplitude ≥ 1.0) were classified into eight phases, and phase-wise frequency during JJAS was computed and normalized by total active days to obtain fractional occurrence.

For each La Niña year, the phase preference and the frequency of MJO occurrence in each phase during the JJAS season were computed as the total number of active days in that phase. The phase counts were then aggregated across all La Niña years and normalized by the total number of active MJO days to obtain fractional phase occurrence (Fp) as-

Where is the number of active MJO days in phase p (Anandh and Vissa, 2020).

To depict the preferential occurrence of MJO phases during La Niña years, a polar phase-frequency diagram was constructed for the JJAS season. For each phase, the total number of active MJO days during the JJAS season was computed by aggregating across all La Niña years. These phase-wise counts were then normalized by the total number of active MJO days to obtain fractional phase frequencies, following earlier studies on MJO phase preference and seasonal modulation (Zhang, 2005; Pai et al., 2011). The normalized frequencies were plotted on a polar coordinate system, with each sector corresponding to an MJO phase and the radial magnitude representing the relative occurrence of each phase.

MJO phases 2–4 were classified as favorable phases, typically associated with enhanced convection and rainfall over the Indian region, while phases 6–8 were classified as unfavorable phases, corresponding to suppressed convection (Krishnamurthy and Shukla, 2007; Pai et al., 2011). A wet–dry dominance index was defined as the difference between the fractional occurrence of favorable and unfavorable phases (Goswami and Xavior, 2005).

To assess the statistical robustness of the observed wet–dry dominance, a bootstrap resampling approach was employed. Phase-fraction data from La Niña years were resampled with replacement for a large number of iterations (10,000 iterations), and the wet–dry difference was recalculated for each iteration to generate a bootstrap distribution (Efron and Tibshirani, 1994). This resampling preserves the sample size while allowing estimation of sampling variability in the wet–dry contrast. The observed wet–dry difference was then compared against the bootstrap distribution to assess statistical significance. The p-value was estimated as the proportion of bootstrap realizations whose absolute magnitude was equal to or greater than the observed value. Statistical significance at the 95% confidence level was determined based on this p-value threshold (p < 0.05). Visualization of the bootstrap distribution, with the observed value marked, provides a clear assessment of the significance of favorable phase dominance during the La Niña JJAS season (Wilks, 2006).

Year-wise and MJO phase-wise persistence values were first computed from the daily MJO phase classification by identifying the number of consecutive days during which the MJO remained in a specific phase (phases 1–8) during each JJAS season. Only days during which the MJO was in its active state, defined by an amplitude greater than or equal to 1, were considered for the analysis. A persistence event was defined as uninterrupted occupancy of a given MJO phase without transition to any other phase under the active MJO condition, and the duration of each such event was calculated in days. These durations were then aggregated for each phase to obtain the phase-wise persistence index for every year.

These year-wise and MJO phase-wise persistence values were then correlated with ISMR (Pai et al., 2014) anomalies for the corresponding JJAS season. The relationship between MJO phase persistence and ISMR was assessed using the Pearson correlation coefficient (r) for each of the eight MJO phases independently (Pearson, 1895). The statistical significance of the correlations was evaluated using a two-tailed Student’s t-test, and p-values were computed to assess significance at the 95% confidence level (Student, 1908; Wilks, 2011). Similar approaches have been widely adopted to quantify MJO–monsoon teleconnections on intraseasonal time scales (Krishnamurthy and Shukla, 2007; Pai et al., 2011).

2.3 Composite analysis of wet and dry La Niña years

A composite analysis was performed for wet and dry La Niña years based on ISMR anomalies. To emphasize robust extremes and minimize the influence of near-normal conditions, the four highest rainfall years (1975, 1988, 2007, 2020) were selected to construct the wet composite, and the four lowest rainfall years (1974, 1985, 1999, 2000) were used for the dry composite. This selection ensures that the composites represent robust extremes within La Niña conditions, thereby highlighting the mechanisms responsible for contrasting monsoon outcomes. Composite fields were then generated for key atmospheric and oceanic variables to examine the dynamical differences between these two categories. The statistical significance of the composites was assessed using a Student’s t-test at the 95% confidence level to highlight statistically significant features in the analysis.

2.4 Phase-wise evolution of MJO-related gill-type circulation

To examine dynamical mechanisms, phase-wise composites of circulation anomalies were constructed using daily wind fields at 850 hPa. Daily anomalies were computed relative to the 1991–2020 JJAS climatology. Composite analysis was performed for each MJO phase to capture the associated large-scale circulation patterns, including equatorial Kelvin and Rossby wave responses, following established Gill-type diagnostics (Gill, 1980; Wheeler and Kiladis, 1999; Zhang, 2005; Hersbach et al., 2023). The resulting patterns illustrate the MJO phase-wise systematic eastward propagation of circulation anomalies and the modulation of the ISMR during La Niña conditions. The Gill-type diagnostics are used here as part of a multi-parameter framework, complemented by circulation and moisture budget analyses, to provide a physically consistent interpretation of MJO–monsoon interactions.

2.5 Vertically integrated moisture flux divergence(VIMFD)

VIMFD was computed using zonal wind (u), meridional wind (v), surface pressure and specific humidity (q) obtained from the Copernicus Climate Change Service (C3S) Climate Data Store (Hersbach et al., 2023). As most of atmospheric water vapour resides below 300 hPa (Fasullo and Webster, 2003) the VIMFD was computed by integrating from the surface (sfc) to 300 hPa:

Positive values indicate moisture divergence, while negative values indicate convergence. Daily anomalies were computed, and phase-wise composites were generated for active MJO conditions (amplitude ≥ 1) for wet and dry La Niña years.

3 Results

3.1 Interannual variability of Indian Summer Monsoon Rainfall during La Niña

To establish the background evolution of ENSO conditions during the study period, the monthly variability of SST anomalies over the Niño3.4 region was examined for La Niña years. La Niña events are defined following the operational criterion of the NOAA Climate Prediction Center, wherein the three-month running mean of Niño3.4 SST anomalies remains ≤ − 0.5 °C for at least five consecutive overlapping seasons [NOAA Climate Prediction Center (CPC), 2024; Trenberth, 1997]. Consistent with the focus on the monsoon season, La Niña years are further identified based on the persistence of negative SST anomalies during JJAS, ensuring that the cold ENSO signal is sustained during the core monsoon period.

The temporal evolution reveals several years in which negative SST anomalies persist across multiple consecutive months, satisfying the standard La Niña criterion (Figure 1). At the same time, considerable variability is evident in the onset, peak intensity, and decay characteristics of these events. Some years exhibit early development and prolonged persistence of cold anomalies extending through the monsoon season (e.g., 1988 and 2010), whereas others show weaker or delayed evolution (e.g., 1974 and 1985), consistent with earlier ENSO studies (Kessler, 2002; McPhaden, 2004).

Figure 1

This diversity in the temporal structure of La Niña events has important implications for the ISMR. Strong and persistent cold SST anomalies can reinforce large-scale circulation changes, such as a strengthened Walker circulation, favoring enhanced monsoon rainfall over India (Webster et al., 1998; Ashok et al., 2003). However, several La Niña years are associated with deficient rainfall, indicating that ENSO forcing alone does not fully determine monsoon outcomes. Instead, interactions between ENSO-related background conditions and intraseasonal variability play a crucial role in shaping ISMR anomalies.

To further quantify this relationship, the interannual variability of detrended JJAS Niño3.4 SST anomalies and corresponding ISMR percentage anomalies was analyzed for the period 1951–2022 (Figure 2). While negative SST anomalies clearly identify La Niña conditions, the corresponding ISMR response exhibits substantial variability, ranging from strong excess to severe deficit years. Several La Niña years are associated with positive ISMR anomaly, indicating a favorable large-scale background for enhanced monsoon circulation. In contrast, other La Niña years (e.g., 1974, 1985, 1999, and 2000) correspond to negative ISMR anomalies, demonstrating that La Niña does not necessarily guarantee a wet monsoon season. Overall, these results highlight that ISMR during La Niña years spans a wide range from excess to deficit conditions despite the presence of persistent cold SST anomalies.

Figure 2

3.2 Influence of MJO phase, frequency, and persistence on ISMR

The all-India JJAS rainfall anomalies during La Niña years show substantial interannual variability, with some seasons experiencing significant excess rainfall while others show deficits. To understand the causes of this variability and the mechanisms that modulate the ISMR during La Niña events, the role of intraseasonal phenomena, such as the MJO, must be considered, as it can strongly influence the active and break phases of the Indian monsoon.

3.2.1 MJO phase characteristics

Understanding the phase-wise distribution of active MJO events during La Niña summers is essential to identify preferred regions of convection and their potential influence on monsoon variability. The analysis reveals a distinctly non-uniform distribution of active MJO phases during JJAS, with the highest occurrence in Phase 1 (~25%), followed by Phase 2 (~22%). Moderate occupancy is observed in Phase 5 (~15%) and Phase 8 (~10%), while Phases 6 and 7 exhibit the lowest frequencies (<7%), and Phases 3 and 4 show relatively weak representation. The phase preference further indicates enhanced MJO residence over the Indian Ocean (Phases 1–2) and intermittent persistence over the Maritime Continent (Phase 5), with reduced occurrence over the western Pacific (Phases 6–7) (Figure 3).

Figure 3

To further distinguish the role of MJO activity in modulating monsoon variability, the total number of active MJO days (RMM amplitude ≥ 1) during JJAS was examined separately for wet and dry La Niña years. A clear contrast emerges between the two categories. Wet La Niña years exhibit substantially higher MJO activity in phases 2 (88 days), 3 (20 days), and 4 (38 days), which are typically associated with enhanced convection over the Indian Ocean and the monsoon region. In contrast, dry La Niña years show markedly reduced activity in these phases, particularly in phase 2 (26 days) and phase 3 (5 days).

Conversely, suppressed phases such as 6 and 8 occur more frequently during dry years, with phase 6 showing a pronounced increase (29 days compared to 7 days in wet years) and phase 8 also being higher (40 days compared to 20 days). This phase-wise asymmetry highlights a systematic shift in MJO activity toward convection-suppressing phases during dry La Niña conditions (Table 1).

3.2.2 Relationship between MJO phase persistence and ISMR

Although favorable MJO phases (2–4) occur marginally more frequently than unfavorable phases (6–8) during La Niña summers, the bootstrap distribution of the wet–dry phase difference (Figure 4a) indicates that this preference is not statistically significant (p ≈ 0.5). The observed difference lies well within the range of internal variability estimated from resampling, suggesting that MJO phase frequency alone is insufficient to explain the occurrence of excess monsoon rainfall during La Niña years.

Figure 4

To further examine this relationship, Figure 4b illustrates the correlation between MJO phase-wise persistence and ISMR anomalies during La Niña years. The correlation coefficients (r) indicate a clear phase-dependent influence of the MJO on monsoon rainfall variability. Positive correlations are observed for MJO Phases 1–5, with the strongest associations in Phases 2 (r = 0.51), 3 (r = 0.33), and 4 (r = 0.48), although these are marginally significant.

In contrast, MJO Phases 6 and 7 exhibit strong and statistically significant negative correlations with ISMR (Phase 6: r = −0.59, p < 0.05; Phase 7: r = −0.64, p < 0.05). Phase 8 also shows a moderate negative correlation (r = −0.41), however, this relationship is not statistically significant and therefore cannot be considered robust. The bootstrap results indicate that differences in MJO phase frequency between wet and dry conditions are not statistically significant. In contrast, persistence-based correlations show a clear and significant relationship with ISMR, highlighting that the duration of continuous MJO residence is more important than phase occurrence in determining monsoon variability. Overall, the results highlight a dipole-like pattern of MJO influence during La Niña years, in which early MJO phases (1–4) tend to support good monsoon rainfall, while later phases (6–7) are associated with rainfall suppression. This phase-dependent modulation underscores the importance of MJO propagation characteristics in explaining interannual variability of the ISMR during La Niña conditions.

3.2.3 Role of MJO phase occurrence and persistence

The year-wise persistence of MJO phases during JJAS provides insight into their role in modulating ISMR variability across La Niña years Figure 5a. Based on the classification described in the methodology, clear contrasts emerge between Wet (1975, 1988, 2007, 2020) and Dry (1974, 1985, 1999, 2000) La Niña composites in terms of the dominance and persistence of specific MJO phases. The Wet composite years are characterized by sustained persistence in active MJO phases (1–4) or by a weak or intermittent presence of suppressed phases (6–8), indicating conditions favorable for enhanced monsoon rainfall Figure 5b.

Figure 5

In contrast, the Dry composite years exhibit markedly different characteristics, with either a lack of persistence in active MJO phases (2–4) or enhanced residence in suppressed phases (6–8) (Figure 5a). In several cases, the occupation of key active phases is minimal, while the MJO signal is dominated by suppressed or transitional phases with limited contribution from phases 2–4. These results highlight that both the presence of active MJO phases and the absence of their sustained persistence play a critical role in determining seasonal rainfall anomalies during La Niña years (Figure 5a, Figure 5b).

The preceding analysis demonstrates that not only the occurrence but also the persistence of favorable MJO phases play a key role in determining seasonal rainfall anomalies during La Niña years. To further understand the underlying dynamical mechanisms, composite analyses were performed for wet and dry La Niña conditions, representing contrasting ISMR regimes. This approach provides a more robust and generalized assessment of the associated circulation features.

3.3 MJO phase persistence and associated dynamical characteristics during wet and dry La Niña years

To understand how differences in MJO phase persistence translate into spatial rainfall variability, composite rainfall patterns were examined for wet and dry La Niña years. A clear contrast emerges in both the distribution and intensity of ISMR between the two categories. During wet La Niña years, widespread positive rainfall anomalies are observed across most parts of India, with particularly strong signals over central India, the Western Ghats, and the monsoon core zone. Many of these regions also exhibit statistically significant anomalies, indicating robust and consistent enhancement of rainfall. In contrast, dry La Niña years are characterized by pronounced negative rainfall anomalies, especially over central and northwestern India, where large contiguous regions show significant deficits, reflecting a substantial weakening of the monsoon. The difference composite (wet minus dry) further emphasizes this contrast, showing strong positive anomalies over central and peninsular India, suggesting that these regions contribute most to the interannual variability of ISMR during La Niña years (Figures 6a–c).

Figure 6

3.3.1 Walker circulation during contrasting La Niña years

To investigate the dynamical mechanisms underlying the contrasting rainfall patterns, the vertical structure of circulation anomalies was examined for wet and dry La Niña composites. The analysis reveals clear differences in the large-scale overturning circulation between the two categories. During wet La Niña years, strong ascending motion (negative anomalies) is evident over the Indian Ocean region (~60°E–100°E), extending from the lower to upper troposphere. This ascent is accompanied by coherent upward-directed wind vectors, indicating well-organized deep vertical coupling. In contrast, pronounced subsidence (positive anomalies) is observed over the central and eastern Pacific (~140°E–120°W), forming a well-defined zonal overturning structure (Figure 7a).

Figure 7

During dry La Niña years (Figure 7b), the vertical structure appears weaker and less organized. The ascending branch over the Indian Ocean is reduced in both intensity and spatial extent, while subsidence anomalies extend into parts of the Indian longitudes. The vertical wind vectors indicate weaker upward motion and increased downward components, suggesting reduced vertical coherence. The zonal contrast between ascent and descent regions is also less pronounced, indicating a weakened large-scale circulation. Overall, these results highlight substantial differences in the strength and vertical organization of circulation anomalies between wet and dry La Niña years.

3.3.2 Vertical velocity (ω) and 850 hPa wind anomalies

To further understand the role of low-level circulation in modulating convection and rainfall, the spatial patterns of vertical velocity (ω) and 850 hPa wind anomalies were examined for wet and dry La Niña composites. The results reveal a clear contrast in wind patterns and associated convective activity over the Indian region. During wet La Niña years, strong anomalous westerlies prevail over the equatorial Indian Ocean and extend toward the Indian subcontinent. These winds enhance moisture transport from the Arabian Sea and Bay of Bengal, leading to convergence over central and peninsular India, as indicated by widespread negative (blue) anomalies. Several regions also exhibit statistically significant signals, suggesting enhanced and well-organized convection.

In contrast, dry La Niña years show weakened and less coherent low-level circulation. The westerly flow over the Indian Ocean is reduced and, in some regions, replaced by anomalous easterlies or weak winds, resulting in diminished moisture transport toward India. Positive (red) anomalies over parts of the Indian region indicate suppressed convection and reduced rainfall, along with a lack of well-defined convergence zones. These differences highlight a clear weakening in the strength and organization of low-level circulation during dry La Niña conditions (Figures 8a,b).

Figure 8

3.3.3 MJO phase dependence of kelvin and equatorial Rossby wave

To examine the role of equatorial wave dynamics in modulating MJO-related variability, phase-wise composites of Kelvin (shading) and equatorial Rossby (ER; contours) wave anomalies at 850 hPa were analyzed for wet and dry La Niña conditions (Figure 9). The results reveal notable differences in the structure and organization of wave activity between the two categories. During wet La Niña years, the Kelvin wave signal is relatively strong and continuous, exhibiting clear eastward propagation from the western Indian Ocean toward the Maritime Continent, particularly during phases 2–4. The anomalies are spatially coherent and well organized over the Indian Ocean region. The ER wave patterns also display well-defined large-scale structures, indicating a robust dynamical response.

Figure 9

In contrast, during dry La Niña years, the Kelvin wave anomalies are weaker and less coherent, with disrupted eastward propagation. The ER wave structures appear fragmented and poorly organized, lacking clear large-scale patterns over the Indian Ocean. In both wet and dry composites, symmetric circulation structures about the equator are not evident, and the wave patterns appear tilted and irregular, suggesting deviations from idealized wave behavior. Overall, these differences highlight a weakening and disorganization of equatorial wave activity during dry La Niña conditions.

3.3.4 Vertically integrated moisture flux divergence (VIMFD)

To assess the role of moisture transport and convergence in modulating monsoon variability, phase-wise distributions of VIMFD anomalies were analyzed for wet and dry La Niña conditions. The results reveal distinct differences in the strength and spatial organization of moisture convergence across MJO phases (Figure 10). During wet La Niña years, phases 2–4 exhibit strong and spatially coherent negative VIMFD anomalies (moisture convergence) over central and peninsular India, particularly pronounced during phases 3 and 4. These regions correspond to the monsoon core zone, indicating enhanced moisture availability and active monsoon conditions. Phase 1 shows relatively weak and scattered anomalies, reflecting its transitional nature. In contrast, phases 6 and 7 display positive VIMFD anomalies (moisture divergence), but these are limited in spatial extent and intensity.

Figure 10

During dry La Niña years, the pattern is markedly different. Phases 2–4 show weaker, fragmented, or locally inconsistent convergence signals, indicating reduced effectiveness of climatologically favorable phases. Meanwhile, phases 6–8 are characterized by strong and widespread positive VIMFD anomalies, especially over central and eastern India, suggesting enhanced moisture divergence and suppressed convection. Overall, these results highlight a clear contrast between wet and dry La Niña years in terms of the strength and organization of moisture convergence associated with MJO phases.

4 Discussion

The results presented above reveal that, although La Niña provides a favorable large-scale oceanic forcing, the ISMR response exhibits substantial interannual variability, ranging from excess to deficit rainfall. This indicates that tropical Pacific SST anomalies alone are insufficient to determine monsoon outcomes (Ashok et al., 2004; Pai et al., 2011). One important factor contributing to this variability is the timing and persistence of La Niña evolution. Years characterized by early onset and sustained cold SST anomalies tend to maintain stronger ocean–atmosphere coupling throughout the monsoon season, enhancing large-scale circulation features such as the Walker circulation (Bjerknes, 1969). This, in turn, promotes increased convection over the Indian region and enhanced low-level moisture transport, thereby favoring above-normal rainfall (Webster et al., 1998; Ashok et al., 2003).

In contrast, years with weaker or delayed SST evolution may not effectively reinforce the monsoon circulation during its active phase, resulting in reduced or deficient rainfall. This explains why some La Niña years do not produce enhanced monsoon conditions despite the presence of cold SST anomalies. The coexistence of both wet and dry La Niña years highlights the nonlinear nature of the ENSO–monsoon relationship and suggests that additional processes play a crucial role in modulating ISMR variability. In particular, intraseasonal variability, such as the MJO, significantly influences the distribution and persistence of convection over the Indian region.

A comparison between La Niña years further emphasizes the importance of the temporal evolution of SST anomalies in modulating ISMR variability. La Niña events associated with enhanced monsoon rainfall are generally characterized by early development and sustained cold SST anomalies extending into the JJAS season, whereas deficient rainfall years tend to exhibit relatively late-developing or weaker SST anomalies. This contrast indicates that the effectiveness of La Niña forcing depends not only on its magnitude but also on its phase-locking with the monsoon season. Although ENSO typically peaks during boreal winter, the persistence of SST anomalies into JJAS provides the relevant background state influencing the monsoon circulation.

The analysis of MJO phase occurrence reveals that La Niña conditions modulate the longitudinal distribution of tropical convection by favoring enhanced activity over the Indian Ocean region. The dominance of Phases 1–2 reflects this westward shift in convective activity, consistent with changes in the Walker circulation. While Phase 1 represents a transitional stage with relatively weak convection, Phase 2 is associated with more organized convection over the Indian Ocean and is therefore more relevant for influencing monsoon rainfall (Goswami and Xavior, 2005; Hendon and Wheeler, 2007). However, the relatively lower occurrence of Phases 3–4 considered most favorable for rainfall enhancement indicates that La Niña conditions alone do not guarantee sustained residence in rainfall-efficient phases. The moderate occurrence of Phase 5 and reduced frequency of Phases 6–8 further highlight that phase frequency alone is insufficient to explain seasonal rainfall variability (Pai et al., 2011; Annamalai and Slingo, 2001, and Goswami and Xavior, 2005).

The phase-wise distribution of active MJO days (Table 1) further supports the distinction between wet and dry La Niña years. Wet years are characterized by enhanced occurrence of MJO phases associated with Indian Ocean convection (phases 2–4), whereas dry years show relatively higher occurrence of suppressed phases (6–8). This asymmetry reinforces the earlier finding that favorable monsoon conditions are linked not only to the presence of active phases but also to the reduced influence of suppressed phases. Importantly, the table complements the persistence analysis by demonstrating that both the frequency and duration of MJO phases contribute to ISMR variability, although persistence remains the more dynamically relevant factor.

A key finding of this study is the distinction between MJO phase frequency and persistence. Although favorable phases (2–4) occur slightly more frequently than unfavorable phases (6–8), their lack of statistical significance indicates that frequency alone has limited explanatory power. In contrast, phase persistence shows a stronger and more physically meaningful relationship with ISMR variability. Persistence represents the duration of continuous convective forcing, which is more relevant for sustaining large-scale circulation anomalies and rainfall.

The positive association between persistence in Phases 2–4 and ISMR suggests that sustained convection over the Indian Ocean and Maritime Continent enhances monsoon circulation and rainfall. In contrast, strong negative correlations for Phases 6 and 7 indicate that prolonged residence of the MJO over the western and central Pacific suppresses convection over the Indian region, producing a dipole-like behavior in rainfall response. These findings demonstrate that persistence, rather than frequency, provides a more robust framework for understanding MJO–monsoon interactions.

The physical mechanism underlying this relationship can be explained through large-scale ocean–atmosphere coupling. La Niña conditions strengthen the zonal SST gradient and intensify the Walker circulation, creating a favorable background for enhanced convection over the Indian Ocean. When the MJO persists in Phases 1–4, this background ascent is reinforced, leading to stronger monsoon circulation and increased rainfall. Conversely, persistence in Phases 6–8 shifts convection toward the western Pacific, induces subsidence over India, and weakens the monsoon, offsetting the favorable La Niña conditions.

Composite analysis further provides insight into the spatial and dynamical differences between wet and dry La Niña years. Wet years are characterized by widespread positive rainfall anomalies, strong vertical ascent over the Indian Ocean, and enhanced low-level westerlies, indicating a well-organized and efficient monsoon circulation (Bjerknes, 1969; Webster et al., 1998). In contrast, dry years exhibit weakened ascent, reduced low-level winds, and increased subsidence, reflecting a disrupted circulation system.

The vertical structure of circulation anomalies highlights that wet La Niña years are associated with strong and vertically coherent ascent, indicating an intensified Walker circulation and efficient moisture transport. Dry years, on the other hand, show weaker and less organized vertical motion, limiting convective development. Similarly, the analysis of 850 hPa wind anomalies demonstrates that enhanced low-level westerlies during wet years support moisture convergence, whereas weakened winds during dry years reduce moisture supply and suppress rainfall.

The role of equatorial wave dynamics further supports these findings. During wet La Niña years, stronger and more coherent Kelvin wave propagation and well-organized equatorial Rossby wave structures enhance large-scale convergence and convection over the Indian Ocean. In contrast, dry years exhibit fragmented and weaker wave structures, limiting the organization of convection. The absence of symmetric equatorial wave structures in both cases reflects the influence of the monsoon background flow, which modifies the classical wave response (Wang and Xie, 1997; Kiladis et al., 2009; Zhang, 2005).

The moisture budget perspective, as reflected in VIMFD anomalies (Figure 10), provides a unifying framework. Wet La Niña years are characterized by strong and coherent moisture convergence during MJO Phases 2–4, whereas dry years exhibit weakened convergence and enhanced divergence during Phases 6–8. This demonstrates that the efficiency of moisture transport is strongly controlled by MJO phase persistence.

Despite these insights, certain limitations remain. The threshold focuses on strong MJO events and may exclude weaker but dynamically meaningful variability. Composite averaging may smooth regional-scale heterogeneity in circulation and convection. The Gill-type composite approach, while useful for diagnosing convectively coupled equatorial wave responses, represents an idealized linear framework and may not fully capture nonlinear ocean-atmospheric coupling processes. In addition, the present analysis focuses on the eastward-propagating MJO and does not explicitly account for monsoon intraseasonal oscillations (MISO), which are characterized by northward propagation and play a crucial role in modulating active–break cycles of the ISMR (Dey et al., 2022). Future research should therefore consider incorporating MISO diagnostics alongside MJO to provide a more comprehensive understanding of intraseasonal variability. Furthermore, research should employ longer datasets and coupled model simulations to better quantify nonlinear ENSO–MJO interactions and their influence on zonal–vertical circulation. Incorporating persistence-based MJO diagnostics into subseasonal prediction systems may improve seasonal monsoon forecasting skill, while further investigation of tropical wave dynamics under changing climate conditions could enhance understanding of multiscale monsoon variability.

Taken together, these results demonstrate that ISMR variability during La Niña years arises from a multi-scale interaction between interannual ENSO forcing and intraseasonal MJO dynamics. While La Niña establishes a favorable large-scale thermodynamic background, the persistence, phase, and spatial organization of MJO convection determine whether this background state is effectively translated into enhanced rainfall. Thus, the modulation of moisture convergence, wave dynamics, and atmospheric circulation by MJO phases emerges as the key mechanism controlling the occurrence of wet and dry La Niña monsoon years. While these results highlight robust statistical associations, the inferred mechanisms should be interpreted as physically consistent relationships rather than direct causal linkages.

5 Conclusion

This study examines the role of MJO characteristics in modulating ISMR during La Niña years. Although La Niña provides a favorable large-scale background for enhanced monsoon activity, the results demonstrate substantial interannual variability in rainfall, indicating that ENSO forcing alone is insufficient to determine seasonal outcomes.

A key finding of this study is that MJO phase persistence, rather than phase frequency, serves as a more robust and physically meaningful indicator of ISMR variability. The duration of continuous residence in specific phases governs the effectiveness of convective forcing and its interaction with large-scale circulation, thereby influencing seasonal rainfall anomalies.

Importantly, the results show that the relationship between MJO phases and ISMR is not governed by the presence of individual phases alone, but by their combined behavior. Excess monsoon rainfall during La Niña years is generally associated with sustained persistence of favorable phases (2–4) together with a reduced or intermittent occurrence of suppressed phases (6–7). In contrast, deficit rainfall conditions arise not only from the presence of suppressed phases but also from the absence or weak persistence of favorable phases. This highlights that both reinforcement by active convection and the absence of suppression are essential for determining monsoon outcomes.

The dynamical analyses further indicate that variations in vertical motion, low-level winds, moisture transport, and equatorial wave activity act as key pathways through which intraseasonal variability modulates the large-scale La Niña background. These processes collectively determine whether favorable oceanic conditions are effectively translated into enhanced rainfall over India.

Overall, the study emphasizes that ISMR variability during La Niña years is governed by a multi-scale interaction between interannual ENSO forcing and intraseasonal MJO dynamics. Importantly, incorporating MJO phase persistence and phase transitions into subseasonal-to-seasonal (S2S) prediction frameworks can potentially improve the skill and reliability of operational monsoon forecasting. Future studies using longer datasets and coupled model simulations are required to further quantify these interactions and enhance predictive understanding of monsoon variability under changing climate conditions.

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