Does the El Niño-Southern Oscillation Impact on the Indian Summer Monsoon 1-Dimensional? Quantifying the Role of Antecedent Southwestern Indian Ocean Capacitance on the Variability of Summer Monsoon Rainfall over Homogeneous Regions of India

Recent rapid changes in the global climate and warming temperatures increase the demand for local and regional weather forecasting and analysis to improve the accuracy of seasonal forecasting of extreme events such as droughts and oods. On the other hand, the role of ocean variability is at a focal point in improving the forecasting at different time scales. Here we study the effect of Indian Ocean mean sea level anomaly (MSLA) and sea surface temperature anomalies (SSTA) on Indian summer monsoon rainfall during 1993-2019. While SSTA and MSLA have been increasing in the southwestern Indian Ocean (SWIO), these parameters' large-scale variability and pre-monsoon winds could impact the inter-annual Indian monsoon rainfall variability over homogeneous regions. Similarly, antecedent heat capacitance over SWIO on an inter-annual time scale has been the key to the extreme monsoon rainfall variability from an oceanic perspective. Though both SSTA and MSLA over SWIO have been inuenced by El Niño-southern oscillation (ENSO), the impact of SWIO variability was low on rainfall variability over several homogeneous regions. However, rainfall over northeast (NE) and North India (NI) has been moulded by ENSO, thus changing the annual rainfall magnitude. Nevertheless, the impact of ENSO on monsoon rainfall through SWIO variability during the antecedent months is moderate. Thus, the ENSO inuence on the atmosphere could be dominating the ocean part in modulating the inter-annual variability of the summer monsoon. Analysis shows that the cooler (warmer) anomaly over the western Indian Ocean affects rainfall variability adversely (favourably) due to the reversal of the wind pattern during the pre-monsoon period.


Introduction
The Indian summer monsoon variability on the inter-annual and intraseasonal time scale has puzzled the scienti c community due to its complex, regionally heterogeneous variability Rajeevan et al., 2012). With innovative technological advancement and many years of research, most dynamic and statistical models fail to predict the extremes and intra-seasonal monsoon rainfall variability with reasonable accuracy (Singh et al., 2019;Pradhan et al., 2017). This is due to the unpredictable variability within the monsoon system and the lack of understanding of the ocean's role in Indian monsoon variability. It is also expected to the persistent ambiguity on the impact of the slowly responding ocean surface to the extreme atmosphere-ocean coupled phenomena such as El Niñosouthern oscillation (ENSO) during the antecedent months and its imprint on the rainfall variability of the following year (Lau and Wang 2006;Heidelberg et al., 2005;Kug and Kang 2006). Thus, monsoon variability over homogeneous regions has not been examined despite the enhanced availability of observational data, especially satellite-derived sea surface temperature anomalies (SSTA), mean sea level anomalies (MSLA), and wind data over the global ocean extensively.
The Asian monsoon circulation in uences most of the tropics and subtropics of the eastern hemisphere and more than 60% of the Earth's population (Webster et al., 1998;Huang et al., 2016). Monsoon variations, mainly unanticipated, impart signi cant economic and social damages and consequences. Its failure often brings famine to affected regions, and strong monsoon years can result in devastating suggested that the interdecadal variability of sea level at Mumbai mimicked the variability in rainfall over the Indian subcontinent. The seasonal river out ow of the monsoon rainfall into the seas around India, and the dynamics of currents along the Indian coast, provides links between the rainfall over the Indian subcontinent and the sea level along the coast of India, with coastal salinity playing an intermediate role.
Nevertheless, the reverse effect has not been studied for the monsoon rainfall variability. Similarly, ocean mean temperature, representing the heat energy of the upper ocean over the southwestern Indian Ocean (SWIO) during months of the same year, shows a strong statistical relationship with the AISMR (Ali et al., 2015). Furthermore, Venugopal et al. (2018) statistically illustrated that ocean mean temperature during January, February, and March over the SWIO could be a better ocean parameter for AISMR seasonal forecasting and variability during normal synoptic conditions. On the other hand, distinct impacts of short-and long-time uctuations of the Indian Ocean surface wind elds, particularly over the SWIO, led to changes in the rainfall over homogeneous regions of India (Yangxing et al., 2020).
While monthly, seasonal, and regional rainfall contributes to the magnitude of AISMR, rainfall during July-August contributes to the total extent of seasonal rain, regardless of the strength (strong or weak) of the shown that the synoptic variability, considered noise, is predictable and has maximum contribution to the seasonal AISMR anomaly. Therefore, AISMR is a highly predictable system on a seasonal time scale. Furthermore, these synoptic activities and MISO, which are smaller in magnitude and affect the intensity of rainfall, are found to be associated with the planetary scale circulations like Madden-Julian Oscillation, ENSO, Indian Ocean Dipole, Paci c Decadal Oscillation and North Atlantic Oscillation (Krishnamurthy et al., 2014;Liebmann et al., 1994;Maloney and Hartmann, 2000;Saji et al. 1999;Sung et al., 2006). Thus, the predictability of AISMR lies on the planetary scale events, which evolve on a longer time scale and may leave their signature and impacts on the smaller-scale events to persist for a longer time (Saha et al., 2019).
While summer monsoon is a coupled atmosphere-ocean phenomenon, recent studies emphasised the role of air-sea interactions over southwestern and equatorial Indian Ocean as the key for the better understanding and forecasting of magnitude and variability of summer monsoon over India (Ali et al., 2015;Venugopal et al., 2018;Thandlam et al., 2020). Some of these studies stressed using new statistical techniques and parameters such as the strength of the winds, ocean heat content and ocean integrated subsurface temperatures in the seasonal forecasting of the summer monsoon (Yangxing et al., 2020; Venugopal et al., 2018). Though these studies found a relationship between summer monsoon and the upper ocean parameters over the SWIO concerning preceding months of the same year, none has focused on the relationship between rainfall over homogeneous regions of the Indian landmass and the SWIO. The question here is, does the Indian Ocean variability affect the entire Indian mainland ISMR? and how does the ENSO would affect this relationship? To the best of our knowledge, no studies are available about how the Indian Ocean variability impacts the different sub-divisions of India, popularly known as homogeneous rainfall. The high ocean capacitance could hold the signature of planetary-scale events to persist for a more extended period, thus impacting the synoptic conditions in the following years. Hence, studying the role of antecedent upper ocean capacitance over the SWIO on AISMR and other homogeneous regions with and without the impact of planetary-scale events like ENSO could provide more insights into the in uence of the air-sea interactions on synoptic conditions over this region.
Furthermore, exploring the effect of the SWIO capacitance on homogeneous rainfall regions of India could give a more localised glance at physical processes and alter air-sea interactions due to recent climate change over these regions to changes in extreme oods and droughts (Sunitha et al., 2021, Thandlam et al., 2019). Also, quantifying the set of atmospheric and ocean parameters in seasonal numerical weather forecasting systems such as ECMWF's new long-range forecasting system SEAS5 is at high priority to improve the forecast precision.
On the other hand, a wide range of con icting results has been described between Indian Ocean SST anomalies and Indian continental rainfall anomalies, which may partly arise because of uncertainties in our knowledge of Indian Ocean SST (Vecchi and Harrison, 2004). Much of the Indian Ocean is not well observed during various periods, either by satellite observations or ships and drifting buoys (Reynolds and Smith, 1994;Reynolds et al., 2002). Many studies have noted that statistical relations can be different in these modern decades than in earlier decades (Hastenrath, 1987;Torrence and Webster, 1999;Clark et al., 2000;Krishnamurthy and Goswami, 2000). The advent of satellite altimetry and microwave techniques to measure Sea Level Anomaly (SLA) and SST, respectively, have provided data sets with better spatial and temporal coverage over the Indian Ocean region in recent times.
In this study, we use these high spatial resolution datasets during 1993-2019 to determine the relationship between AISMR and SLA, SST before and after removing the Nino3.4 SST anomaly (SSTA) effect. A robust and distinct relationship with these parameters in the Indian Ocean has been found in recent years, especially after 2001 (Shi-Yun et al., 2021). We examined the role of these parameters over the SWIO in the rainfall variability of different homogeneous regions of India. Section 2 describes the datasets used and the methodology for the study. Subsequently, section 3 describes results and discussions. Finally, the conclusions are summarised in section 4.

Data
In recent times many studies have focused on the external forcing effect of Indian Ocean SST, especially from Arabian Sea SST to the Indian summer monsoon rainfall variability (Shukla 1975; Goswami and Rao, 1988;Vecchi et al. 2004;Kothawale et al. 2008). However, very few studies on the subsurface oceanic effect on the Indian summer monsoon rainfall. Under favourable conditions, the subsurface ocean thermal structure and heat content in uence the air-sea interactions and the lower troposphere moisture content. Hence, the subsurface features in the SWIO, which are in the direct vicinity of the crossequatorial winds before and during the summer monsoon, affect the rainfall over the land. Particularly, ocean mean temperature during January-March over the SWIO up to 26 o isotherm derived from satellite altimeter data with great accuracy and has become an important parameter to study the variability of AISMR (Ali et al., 2015;Venugopal et al., 2018). However, despite satellite data, one of the discrepancies in using ocean data products such as ocean mean temperature and ocean heat content is the availability of reliable subsurface temperature and salinity data.
Meanwhile, sub-surface temperature and salinity pro le data from scattered XBT, CTD, ARGO, and buoy's locations were previously available with spatial and temporal sampling errors. However, ARGO has recently made revolutions in this aspect by measuring T/S pro le data since 2001 over the global ocean, including the Indian Ocean from 2003 (Gould et al., 2004;Ravichandran et al., 2004). Although the spatial distribution pattern of ARGO pro ling oats was sparse during the initial phase, it has reached its objective to have at least one pro le in a 3 x 3-degree domain in 2008 over the global ocean, including the Indian Ocean (Meyssignac et al., 2019). The other most reliable subsurface information comes from the sea level measurement since it shows the mirror image of subsurface variation in the surface. This data has been available from satellite altimetry with high spatial resolutions since 1992.
The high-resolution (0.25x0.25) blended analysis of daily Optimum Interpolation SST (OISSTv2.1, also known as Reynolds' SST) obtained from the National Oceanic and Atmospheric Administration (NOAA) during 1993-2019 (Reynolds et al., 2007) has been used in the study. In addition, we used delayed-time (reprocessed) daily SLA data for the 1993-2019 period with a spatial resolution of 0.25°, obtained from Copernicus Marine Environment Monitoring Service (CMEMS) (Rosmorduc et al., 2013). This product is obtained by combining fully processed data from various altimeter missions (Topex/Poseidon, ERS-1/2, Jason-1, Envisat and OSTM/Jason-2). The daily AISMR data has been extracted from the high-resolution (0.25 × 0.25) daily rainfall data constructed from more than 7000 rain gauge stations around India during the study period Pai et al., 2014). The AISMR data used in the study show a lower seasonal magnitude of rainfall than the data from Rajeevan et al., (2006) which has the 1 o x1 o resolution ( gure not shown). The bias could be due to the annual changes in the number of rain gauge stations used in constructing the data. Despite its lower magnitude, the dataset has been used in the study owing to its higher spatial resolution. Based on the rainfall intensity and variability, homogeneous regions of AISMR are broadly divided into north India (NI), east India (EI), northeast India (NE), central India (CI), and WCI (Yangxing et al., 2016; Rahman and Sengupta 2007). Figure 1 shows the mean climatology of AISMR (June-September) during 1993-2019 in colour and the homogeneous rainfall regions. More details on the regions are provided in table 1. Similarly, the Nino3.4 (170W-120W and 5S-5N) region's monthly SSTA indices for ENSO were obtained from the Royal Netherlands Meteorological Institute climate explorer (Rayner et al., 2003). In addition, monthly surface zonal and meridional wind anomalies constructed from ECMWF-ERA5 with 0.25x0.25 resolution has been used in the composite analysis (Hersbach et al., 2017). Table 2 shows the details of the data used in the study. Among the ve homogeneous regions considered in the study, WCI and NE have the highest seasonal rainfall ( gure 1) and standard deviation (Table 1), followed by EI, CI and NI. Hence, a seasonal departure from average rainfall over WCI and NE could in uence the total AISMR magnitude. On the other hand, being a region with low standard deviation, the CI receives consistent seasonal rainfall and is also crucial for the absolute magnitude of the AISMR as CI covers a larger area than any other homogeneous region considered in the study. To identify the years of anomalous monsoon rainfall, we adopted Mooley and Parthasarathy's procedure (1983). The standard deviations for AISMR and the homogeneous regions of summer monsoon rainfall (June-September) were computed in the following way Where "σ i " is the standard deviation of rainfall during the i th year, "R i " is the monsoon rainfall of the i th year, "R m " is the mean rainfall, and "σ m " is the standard deviation of the monsoon rainfall during 1993-2019. These standard deviation values categorise monsoon rainfall as de cient or excess.
De cient= σ i < -0.80 The monthly anomaly elds for SLA and SST were constructed by computing monthly mean SLA and SST and then subtracting it from monthly climatology during 1993-2019. Table 3 shows the details of normal, de cit and excess monsoon years during the study period. Figure 2 shows the spatial pattern of summer monsoon rainfall standard deviation during 1993-2019. The regions with a standard deviation less than 4 mm/day in southern peninsular India, the northernmost part of India and northwestern India are considered rain shadow regions. These regions receive less rainfall during the summer monsoon. Thus, these regions' contribution to AISMR magnitude is minimal, not regarded as homogeneous regions.  (supplementary gures S1a, S1b). Thus, a slight change in the magnitude of these parameters over SWIO could lead to signi cant changes in air-sea interactions in the lower troposphere, cloud cover, variability in radiation and turbulent uxes over this region. Though the difference in half-year wind speeds is moderate (supplementary gure S1c) over the region, the atmosphere saturated with water vapour and the in ux of continuous moisture from the ocean into the atmosphere would lead to a healthy monsoon season.
During winter half-year, the southward movement of the intertropical convergence zone could lead to less insolation and ocean warming and change SSTA and MLSA over SWIO. Therefore, these changes in insolation and air-sea uxes coupled with altering wind patterns would induce differences in SSTA and MSLA along the SWIO ( gure S2). The increase in insolation due to northward propagation of intertropical convergence zone and increased wind speeds due to land-sea pressure gradient cause SSTA and MSLA to increase in summer months over SWIO ( gure S2c and S2d). Nevertheless, SSTA standard deviation over SWIO is the highest in the Indian Ocean in winter half-year and summer months before summer monsoon full onset over India ( gure 5a and 5b). During March-June, the large variability is associated with a robust cross-equatorial ow of winds over this region. This substantial variability on the positive side over the region favours the evaporation and the magnitude of precipitation received over land in the following months (June-September). MSLA, on the other hand, shows moderate standard deviation values over the SWIO but has a signi cant standard deviation over the Seychelles-Chagos thermocline ridge region (Vialard et al., 2009) propagating from the southeast during winter half-year ( gure 5c). The magnitude has become stronger during the summer months and reach the SWIO ( gure 5d).
To identify the regions of large SSTA and MSLA variability which in uences the monsoon rainfall, we choose the standard deviation of AISMR as an indication of large-scale monsoon activity. Xie et al. (2002) have shown that the thermocline variability over the SWIO affects the SST variability. This variability is due to the remotely controlled dynamical effects by phenomena like ENSO over the Paci c Ocean and IOD over the Indian Ocean ). However, the effect may not be robust where the air-sea interaction process dominates the SST variability, especially over the SWIO, Arabian Sea and Bay of Bengal. Our results also corroborate this. Figure 6 shows the spatial correlation pattern between SSTA and SSHA. The correlation is high over the Seychelles-Changos thermocline ridge region. It is to be noted that simultaneous and lagged correlation patterns retain almost similar features; this suggests that the effect of SSHA on SSTA is due to the change of thermocline depth over this region.

Correlation between AISMR standard deviation and SSTA
We correlated the standard deviation of AISMR with the preceding year (-1) June month to current year (0) September month's SSTA and MSLA over the Indian Ocean. Figure 7 shows the spatial lagged correlation with values higher than +/-0.2 signi cant at 95% level between AISMR standard deviation and SSTA. The  Figure  9 shows the time series (red line) of lagged correlation between SSTA over SWIO and regional rainfall over the ve homogeneous regions and entire Indian landmass. Out of ve homogeneous regions in gure 9, the monsoon rainfall over the NI, CI and WCI was affected by SSTA changes over SWIO (Vecchi et al. 2004). On the other hand, these regions get most of their annual rainfall only during the southwest monsoon season. They showed rainfall over Gangetic plains, and the west coast of India is related to SST over the south Indian Ocean during Oct-Nov of the previous years.
A large part of the interannual variability of monsoon rainfall is linked to ENSO, a coupled oceanatmospheric phenomenon in the Paci c Ocean. This phenomenon leads to the large-scale displacement of the east-west circulation in the tropics, in uencing global SSTs (Walker, 1918; Pant and Rupa Kumar, 1997). Given this, the impact of ENSO on the relationship between Indian Ocean SSTA/MSLA and AISMR standard deviation has been examined by removing the in uence of the ENSO effect. We consider the Nino3.4 region SSTA as the indicator of the ENSO phenomena. The ENSO effect has been removed following the methods described in sec 2.2.2. Figure  Similarly, Figure 10a shows the monthly lagged cross-correlation between AISMR standard deviation and SSTA averaged over SWIO (40-65 E) along 30S-30N. It shows that the decorrelation length is ~3 months starting from March. These values are signi cant at 95% based on a student's t-test. Monthly lagged cross-correlation between AISMR standard deviation and SSTA averaged over 10S-10N shown in gure 10b shows that the decorrelation length is localised over SWIO with a time length of ~3 months in premonsoon. Both cases highlight the signi cant correlations over SWIO during the preceding autumn and winter months.

Correlation between AISMR standard deviation and MSLA
To know whether it is only due to the air-sea interaction process or subsurface temperature also does have any role, the SSHA from merged Altimeter data has been studied. SSHA variability gives the proxy of sub-surface temperature variability. Figure 11 shows the monthly lagged cross-correlations between AISMR and MSLA from June (-1) to September (0) over the Indian Ocean. This indicates that the mesoscale eddies have a dominant role in the monsoon variability. Their CC values are signi cant at a 90 % level based on the student's t-test analysis. Therefore, the increasing trend in the MSLA over SWIO, as shown in gure 12, potentially in uences the positive trend in SSTA over the region due to strong surface winds, oceanic convective mixing, and air-sea ux exchanges. Mechanisms about how these eddies are in uencing the Indian summer monsoon will be studied in detail in our future studies.
The correlation values over SWIO were increased during preceding October and continued until the current year's May. Thus, the sub-surface effects dominate during pre-monsoon time, as in the case of SSTA. The CC values above 90% signi cant levels are also observed over SWIO. This suggests that air-sea interactions over the surface, subsurface ocean dynamics and thermodynamics play a crucial role in the AISMR variability.
From the time series of AISMR standard deviation and SSHA over SWIO, AISMR standard deviation follows the SSHA variations ( Figure 13). This relationship became robust during recent decades. This may be because Indian Ocean climate variability has become more active during the last couple of decades due to many positive Indian Ocean Dipoles Rao et al. 2008). Like SSTA, the lagged correlation time series between AISMR standard deviation and MSLA over SWIO shows robust SSHA effects on the regional rainfall ( Figure 13). AISMR and SSHA show maximum positive CC values in preceding December and during the current year's May. Except for NE and WCI, rainfall over all other homogeneous regions shows no large coupling with MSLA variability over SWIO. While CC of NE rainfall and MSLA strongly impact ENSO, the CC values over other regions remain unchanged after removing the ENSO effect ( gure 13). However, the impact of MSLA variability over WCI and NE could in uence the AISMR interannual variability due to the large share of the rainfall over the two regions in AISMR magnitude. Accordingly, the seasonal in ow of the monsoon rainfall into the seas around India and the dynamics of currents along the Indian coast provide the link between the rainfall over the Indian subcontinent and the sea level along the coast of India, with coastal salinity playing an intermediate role.

SSTA and MSLA variability during contrasting monsoon
To delineate the extreme monsoon rainfall variability, we have de ned the excess, normal and drought years according to Mooley and Parthasarathy (1983). Figure 15 shows the composite of SSTA, MSLA (shaded) and sea surface wind (vector) for excess, de cit, and normal monsoon years. During excess monsoon years, both SSTA and MSLA are positive over the SWIO, including the Seychelles-Chagos thermocline ridge region. Anomalous weak surface winds (near the east African coast) with the northeasterly ow instead of normal south-westerlies favours these conditions over the region. This anomalous weak wind reduces the upwelling, and the region becomes one of the moisture source regions during the subsequent monsoon months ). The MSLA variability can be taken as a proxy to ocean heat content. The positive MSLA over the entire SWIO with shallow mixed and deep isothermal layers could act as a heat source for the moisture supply.
In contrast, the composite during the de cit monsoon year's shows an opposite spatial pattern of SSTA, MSLA and reversal of winds over the entire Indian Ocean. Both SSTA and MSLA over SWIO, including the Seychelles-Chagos thermocline ridge region, turned unfavourable with strong westerly winds. The negative MSLA signi es the deep MLD and shallow thermocline over these regions. Normal monsoon years are accompanied by the moderate values of SSTA, MSLA and winds, which could lead to dampened air-sea ux exchange between the upper ocean and the lower troposphere over the Indian Ocean. The wind anomalies from National Centers for Environmental Prediction (NCEP) daily reanalysis 2 data with 2.5x2.5 resolution (Kalnay et al., 1996) also show similar results (not shown here).

Conclusions And Discussions
The availability of high spatial resolution satellite altimetry and microwave SST data opened windows to undertake a detailed study of Indian Ocean dynamics and understand the synoptic conditions over the region. Thereby there is an increasing interest in several phenomena in the Indian Ocean that impacts global weather and climate at different temporal and spatial scales. Among others, IOD, MJO and tropical cyclones are a few coupled events in uenced by Indian Ocean synoptic conditions. Similarly, All India summer monsoon rainfall (AISMR) is another major annual event that strongly impacts the atmosphere and oceanic conditions. While the atmosphere controls a large part of the monsoon dynamics, the ocean's role in interannual summer rainfall variability is unavoidable. Several recent studies emphasised the impact of pre-monsoon SST and upwelling over the Arabian Sea and the tropical Indian Ocean On the other hand, contemporary trends in climate change, changes in surface and air temperatures are causing the large interannual variability in AISMR magnitudes, thus causing more frequent droughts and oods. These changes in both atmosphere and oceanic variables also increase the intensities of planetary-scale events such as ENSO. With large heat capacitance, oceans could impact these global-scale phenomena longer and affect the air-sea uxes in the subsequent months. The SWIO is one such region, in uenced by events like MJO, IOD and ENSO.
Though several studies attempted to understand the impact of these processes on the variability of AISMR magnitude, the majority of previous works have either focused on the total variability of AISMR or considered single or multiple ocean parameters to study the contemporary relationship with AISMR. Thus, limited attempts were made to understand the connection between rainfall variability over homogeneous regions and oceanic parameters in the Indian Ocean in uenced by the events like ENSO during the preceding months. Hence nding the relationship between preceding months (previous year June to following year May) Indian Ocean heat capacitance and monsoon rainfall over different homogeneous regions in the following year could improve the monsoon forecasting in the near future. In addition, we also studied the impact of ENSO on the connections among SSTA, MSLA and rainfall over land.
Wind-induced mixing and upwelling over SWIO during May and April (prior to monsoon onset) led to a robust lead-lag correlation between MSLA and SSTA over the region. Thus, the strong coupling between MSLA and SSTA over SWIO led to strong seasonal variability of these two parameters over the region. On the other hand, excess monsoon can be expected with positive MSLA, SSTA and associated low and reverse wind anomalies over the East African coast. Opposite MSLA, SSTA conditions with strong westerlies over SWIO could lead to de cit summer monsoon rainfall. Normal monsoon years are associated with neutral magnitudes of these parameters over SWIO. Finally, the impact of ENSO on the variability of homogeneous rainfall through induced changes in SSTA and MSLA over SWIO was moderate. The weakened feedback of the Indian Ocean on ENSO since the early 1990s could also be a key here (Han and Wang 2021). These ndings signify that SWIO antecedent heat capacitance in SSTA and MSLA is moderate over homogeneous rainfall regions, with some regions showing negative correlations. However, the impact of ENSO on atmospheric conditions over the Indian Ocean could be more signi cant than that on ocean parameters over SWIO in modulating inter-annual variability of AISMR.

Data Availability
All data used in the study are freely available. Rainfall data can be obtained from India Meteorological Department (https://www.imdpune.gov.in/Clim_Pred_LRF_New/Grided_Data_Download.html). Sea surface temperature data was downloaded from National Oceanic and Atmospheric Administration (https://www.ncei.noaa.gov/products/optimum-interpolation-sst  Corresponding author Correspondence to Venugopal Thandlam.

Ethics declarations
The manuscript has not been submitted to more than one journal for simultaneous consideration. The manuscript has not been published previously (partly or in full) unless the new work concerns an expansion of previous work. Our study is not split up into several parts to increase the number of submissions and submitted to various journals or to one journal over time. No data has been fabricated or manipulated (including images) to support our conclusions. No data or text by others are presented as if they were our own ("plagiarism").

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The authors declare no competing interests.       Lagged correlation of AISMR standard deviation with SSTA over the Indian Ocean. Values higher than +/-0.2 signi cant at 95%. The time series of SSTA (solid blue line) over SWIO and AISMR standard deviation (solid orange line). Blue and orange dotted lines represent the SSTA and AISMR standard deviation trends, respectively. Time series of lagged correlation between SSTA over SWIO and regional rainfall over the ve homogeneous regions and entire Indian landmass before (red) and after (blue) removing ENSO effect.    Lagged correlation of AISMR standard deviation with SSTA over the Indian Ocean. Values higher than +/-0.2 signi cant at 90%.

Figure 12
The time series of MSLA (solid green line) over SWIO and AISMR standard deviation (solid orange line).
The green dotted line represents the trend in MSLA.

Figure 13
Time series of lagged correlation between MSLA over SWIO and regional rainfall over the ve homogeneous regions and entire Indian landmass before (red) and after (blue) removing ENSO effect.

Supplementary Files
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