Introduction

The rapid warming of the Arctic has led to a phenomenon known as Arctic Amplification, where temperatures in the region increase at a rate significantly higher than the global average1. This accelerated warming is largely driven by positive feedback loops, such as the ice-albedo effect, water vapor feedback, and lapse rate feedback2. As sea ice cover diminishes, the reflectivity (albedo) of the Earth’s surface decreases, leading to enhanced absorption of solar radiation by the ocean, which in turn accelerates warming3. Additionally, the reduction in sea ice weakens the temperature gradient between the polar and tropical regions, disrupting the atmospheric circulation patterns that have traditionally governed weather systems globally4. This shift in the temperature gradient is particularly important because it influences the dynamics of the jet stream and Rossby waves, leading to more persistent and extreme weather events in the tropics5. Consequently, gaining deeper insights into the influence of Arctic sea ice variability on large-scale atmospheric patterns and its downstream impacts on tropical weather systems is of critical importance.

Several studies have well-established that the decline in Arctic sea ice has a profound influence on mid-latitude weather patterns, which often contributes to colder conditions across northern continents6, influences winter atmospheric blocking events (such as Ural blocking) and modulates weather patterns over Europe and Asia7. Melting Arctic sea ice can also disrupt ocean circulation, affecting weather in Europe and North America by slowing the Atlantic Meridional Overturning Circulation (AMOC)8. In particular, sea ice loss in the Barents–Kara (B–K) sea has been linked to alterations in the polar vortex and stratospheric circulation, which are critical drivers of mid-latitude climate variability9.

Beyond mid-latitude impacts, recent research highlights the role of Arctic sea ice loss in shaping tropical climate systems. For instance, the retreat of Arctic sea ice has been associated with equatorward shifts in the Intertropical Convergence Zone (ITCZ), leading to warming in the tropical upper troposphere10. Additionally, numerical model simulations suggest that Arctic sea ice fluctuations significantly affect the Hadley circulation and equatorial Pacific Sea Surface Temperatures (SSTs), indicating potential global climate repercussions11,12. Studies also report the impact of spring Arctic sea ice on the East Asian Summer Monsoon via teleconnections involving North Pacific SSTs13. Furthermore, early-winter sea ice conditions in the Laptev Sea have been associated with variations in the Indian Ocean Dipole (IOD), which in turn impacts the Indian Summer Monsoon Rainfall (ISMR)14.

The emerging literature reveals growing interest in examining the teleconnections between the Arctic and tropical regions, with a particular emphasis on understanding how Arctic sea ice variability affects the ISMR. For instance, Kulkarni and Agarwal highlight the pronounced link between Arctic sea ice extent and Indian precipitation, emphasizing the importance of further exploring these teleconnections15. Recently, their study demonstrated that Arctic sea ice concentration (SIC) variability in the Atlantic and Pacific sectors influences ISMR patterns through the North Atlantic Oscillation (NAO) and El Niño Southern Oscillation (ENSO)16. While Zhu et al. explored the reverse dynamic, showing that ISMR modulates Arctic sea ice thickness by altering longwave radiation, independent of ENSO events17. These studies underscore the complex bi-directional interactions between the Arctic and Indian monsoon systems.

Such interactions are further amplified by the consequences of Arctic sea ice loss, which alters large-scale atmospheric circulation, modifying Rossby wave propagation and shifting the subtropical jet stream. These changes can significantly affect the ISMR onset and intensity14,18. Furthermore, Arctic changes influence the IOD and Madden-Julian Oscillation (MJO), both critical drivers of ISMR variability11,13. As climate change accelerates Arctic sea ice decline, understanding these complex linkages is crucial for improving seasonal monsoon forecasting and mitigating potential socio-economic impacts across the Indian subcontinent.

Previous studies have shown that October sea ice extent in the B–K sea is closely linked to the following summer’s monsoon rainfall over India, where lower sea ice extent tends to weaken monsoon activity, and higher extent is associated with stronger monsoons19. Besides, the retreat of sea ice in the Kara–Laptev Seas has been associated to an increase in late-season, high-intensity rainfall events over India20. The B–K region, situated to the northwest of Russia, plays a pivotal role in shaping Arctic climate dynamics. This region has been increasingly influenced by the northward expansion of warm Atlantic Ocean waters, a phenomenon known as “Atlantification”21. The anomalous inflow of these warm, saline water disrupts ocean stratification and ventilation, leading to enhanced meridional heat flux and vertical mixing22. These changes are further sustained by shifting atmospheric storm tracks and winds as well as various radiative and climate feedback processes23,24.

Research indicates that sea ice anomalies in the B–K region significantly influence atmospheric circulation patterns, including stratosphere-troposphere coupling25,26. These atmospheric responses have the potential to extend beyond the polar regions, impacting tropical weather systems2,27,28. Despite the well-established role of Arctic sea ice in influencing global and regional climate, the specific impacts of springtime sea ice variability in the B–K sea, often termed as the “Arctic warming epicenter” on the ISMR, remain underexplored29,30. Hence, this study addresses this critical research gap by systematically analyzing the mechanisms through which spring SIC anomalies in the B–K region drive changes in monsoon dynamics over India. By employing a composite analysis of low- and high-sea-ice years, this research provides novel insights into the broader teleconnections between Arctic sea ice conditions and tropical climate variability that modulate changes in large-scale atmospheric circulation patterns and associated teleconnection mechanisms, subsequently influencing the variability of ISMR.

Materials and Methods

Study Area and Datasets

The Arctic, located above 66.5° N latitude, encompasses the Arctic Ocean, which is surrounded by key landmasses and divided into several marginal seas (Fig. 1a). These include the Greenland, Barents, Kara, Laptev, and East Siberian Seas near the Eurasian continent, as well as the Beaufort, Baffin Bay, and Chukchi Seas near the North American continent. The Arctic Ocean connects to the Pacific Ocean via the Bering Strait and to the Atlantic Ocean through the Greenland and Labrador Seas31. Among these marginal seas, the B–K region (20° E–90° E, 65° N–85° N; outlined in black dotted lines in Fig. 1a) has gained increasing attention in recent years due to a significant decline in sea ice, with studies highlighting its amplified warming compared to other Arctic regions32,33. The growing recognition of the B–K region’s central role in driving large-scale atmospheric changes underscores its selection for this study.

Fig. 1
figure 1

Study area and regional classification (a) Map of the Arctic Ocean showing its sub-regions. The Barents and Kara Seas (20° E–90° E, 65° N–85° N) are outlined with dotted lines, representing the domain used to produce the sea ice concentration index in this study (b) Homogeneous regions of India based on rainfall patterns. Figure adopted and modified from34. The map was generated using Python version 3.12 and is available at https://www.python.org/.

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To assess the possible association between springtime (March–May) sea ice anomalies in the B–K region and ISMR, this study relies on SIC data obtained from the ERA5 reanalysis (the fifth generation of ECMWF) spanning from 1959 to 2021. ERA5 is selected due to its long-term availability and advanced data assimilation techniques, offering high spatial (0.25° × 0.25°) and temporal (hourly) resolution. Large-scale oceanic and atmospheric circulation patterns are also analyzed using key variables, including SST, 2-meter air temperature, sensible heat flux, mean sea level pressure (MSLP), wind speed (200 hPa and 850 hPa), and geopotential height (200 hPa and 850 hPa), zonal wind anomaly at 10 hPa. The zonal-mean of the zonal wind at 10 hPa (~ 30 km) and 60°N, which is the westerly wind during winter, is commonly used to detect the strength of the stratospheric polar vortex. To better understand the plausible physical mechanisms linking the B–K region to monsoonal variability, additional variables such as vertical velocity (500 hPa), divergence (500 hPa), and Convective Available Potential Energy (CAPE) were considered. All datasets were sourced at a 6-hourly resolution to capture detailed atmospheric variability and converted into seasonal data using the Climate Data Operator (CDO).

In India, the ISMR is responsible for over 80% of the country’s annual rainfall35. Precipitation patterns exhibit considerable spatial and temporal variability, with the monsoon season being the key period of rainfall. The country is divided into six homogeneous rainfall regions based on these patterns: Northwest, Northeast, Central Northeast, West Central, Peninsular, and the Hilly region (Fig. 1b;36. For the investigation of ISMR, we use daily gridded rainfall data at a 0.25° × 0.25° resolution, spanning 1959 to 2021, sourced from the India Meteorological Department (IMD). This dataset, generated from over 6,000 gauging stations across India, has been widely used in various hydroclimatic studies to capture the detailed characteristics of the ISMR, including its seasonal distribution and orographic effects in the Western Ghats and the Himalayas37. The seasonal mean and maximum rainfall are derived from daily datasets and are referred to as mean and extreme ISMR, respectively.

Methodology

The study employs composite analysis to examine the impact of B–K sea ice anomalies on ISMR patterns. As a first step, an area-averaged SIC index is constructed for the B–K region during the spring season. To eliminate long-term trends, ensure consistency, and isolate the interannual variability, the SIC time series is first detrended by computing and subtracting its linear trend. The resulting series is then standardized to have zero mean and unit standard deviation (see Fig. S1a), enabling uniform comparison across the study period. Fig. S1b depicts the correlation between the absolute sea ice index and ISMR, indicating a significant relationship between Arctic sea ice variability and ISMR. However, to better understand the contrasting atmospheric responses, it is necessary to examine the segregated impacts during low- and high-ice years. Years with standardized SIC anomalies exceeding + 1 standard deviation are classified as “high sea ice” years, while those falling below − 1 standard deviation are categorized as “low sea ice” years. Based on this classification, the identified low sea ice years are 1964, 1965, 1973, 1975, 1992, 1995, 2006, 2007, 2012, and 2016. The corresponding high sea ice years are 1966, 1969, 1978, 1979, 1981, 1982, 1987, 1998, 1999, and 2003.

Composite analysis, also referred to as conditional sampling, is a widely used statistical technique in climate science that helps isolate robust signals associated with specific events or conditions while minimizing unrelated background variability. Originally introduced by Chree et al. in space science, composite analysis has since found broad applications in Earth system studies38,39,40,41. The method involves identifying a set of key events, typically defined as positive or negative phases of an index (here, high/low SIC), and then computing the average of a climate variable over the time steps associated with those events. This procedure enhances recurring patterns associated with the events while noises are averaged out.

In this study, composite analysis is conducted for the June–September (JJAS) season, which marks the peak of the ISMR. Years with anomalously low and high Arctic SIC in the preceding spring are identified as the key events. Composite anomalies for key variables mentioned in the Data section are calculated as deviations from the JJAS climatological mean over the full study period. This enables us to examine how springtime sea ice conditions may modulate large-scale atmospheric circulation and impact monsoon rainfall across the Indian subcontinent. Also, to examine the large-scale circulation response, streamfunction anomalies were computed from wind speed components and geopotential height fields following the formulation of Takaya and Nakamura42.

Results

Composite analysis of ISMR response to Arctic sea ice variability

The composite analysis of mean and extreme ISMR anomalies during low- and high-sea-ice years spanning 1959 to 2021 is illustrated in Fig. 2. During low sea ice years, the Indo-Gangetic Plain, a crucial agricultural and densely populated region (outlined in black dotted lines in Fig. 2a), exhibits widespread negative mean rainfall anomalies, indicating weakened monsoon activity. The Indo-Gangetic Plain, also known as the Northern Plain, is a fertile plain spanning the northern and north-eastern part of the Indian subcontinent and is well known for its agricultural productivity. This reduction in rainfall over this region aligns with a weakened monsoon trough and a diminished land-sea thermal gradient, as reported in previous studies43. However, during these years, positive mean ISMR anomalies are observed across much of India, including Rajasthan, Vishakhapatnam, Nagpur, the Western Ghats, parts of the western Himalayan and northeastern India, suggesting a regional redistribution of rainfall44,45. These anomalies may be associated with localized convective activity and shifts in monsoonal wind patterns, thereby underscoring the complex interactions between Arctic forcing and regional climatic responses over India.

Fig. 2
figure 2

Evolution of Indian Summer Monsoon Rainfall associated with low and high sea ice years spanning 1959 to2021. Composite anomalies of (a) mean and (c) extreme rainfall during low sea ice years (b) Same as (a) but for high sea ice years (d) Same as (c) but for high ice years. Here, mean and extreme rainfall refer to the seasonal mean and maximum rainfall, respectively. Low and high sea ice years are identified based on spring (MAM) sea ice index over the Barents–Kara region, while all rainfall anomalies are computed for the JJAS season. Black dotted lines mark the Indo-Gangetic region in panel (a).

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Conversely, during high sea ice years (Fig. 2b), enhanced monsoon activity is evident, particularly over the Indo-Gangetic Plain and parts of northeast India, where positive mean ISMR anomalies reach up to 140 mm. This increased rainfall is linked to a strengthened monsoon trough and enhanced low-level jet activity, which facilitates moisture advection from the Arabian Sea and the Bay of Bengal46,47. In contrast, regions such as the Western Ghats, northern, western, and southeast India exhibit pronounced negative mean ISMR anomalies, potentially linked to large-scale subsidence and atmospheric divergence, induced by intensified convective activity in adjacent areas48. Extreme ISMR anomalies during low and high ice years follow a similar pattern but with ten times greater intensity, ranging from −1200 to 1200 mm (Fig. 2c, d). This illustrates the significant impact of sea ice variability on extreme rainfall events as well.

Overall, low (high) sea ice years are predominantly associated with reduced (enhanced) rainfall across the Indo-Gangetic plain. The pronounced contrast in ISMR anomalies during low- and high-sea-ice years suggests a strong teleconnection between springtime B–K sea ice variability and the ISMR. Sea ice variability in this region influences large-scale oceanic and atmospheric circulation patterns (discussed in the following sub-sections), such as planetary wave propagation, subtropical jet shifts, and mid-latitude westerlies, which, in turn, affect the strength and position of the monsoon trough over the Indian subcontinent2,49,50. Studies have shown that Arctic Amplification can further enhance these teleconnections, altering the jet stream and Rossby waves, leading to significant changes in monsoon patterns51.

To investigate the statistical consistency of the proposed mechanisms, a cross-correlation analysis was performed between SIC, SST, near-surface air temperature, MSLP, geopotential height at 200 hPa and 850 hPa, and ISMR (Fig. S2). The analysis reveals a robust negative relationship between SIC and SST (r = − 0.95), and SIC and near-surface air temperature (r = − 0.84), highlighting strong surface thermodynamic coupling. In addition, strong positive correlations between upper- and lower-tropospheric geopotential heights (r ≈ 0.95) and their moderate associations with MSLP suggest a vertically coherent atmospheric adjustment that links surface anomalies to large-scale circulation. These results provide concrete statistical evidence that the observed SIC anomalies are systematically connected with both surface and upper-level atmospheric fields, thereby lending statistical support to the teleconnection mechanisms proposed in this study. A more detailed examination of these relationships is carried out in the following subsections using composite analysis of the relevant climate variables.

Composite analysis of thermal and pressure anomalies

This section examines the composite anomalies of SST, 2-meter air temperature and SLP during low and high sea ice years (see Fig. 3). During low sea ice years, pronounced warm SST anomalies are observed over the B–K sea, followed by Laptev Sea, Beaufort Sea, Canadian Archipelago, Baffin Bay and Labrador Sea (Fig. 3a). This warming is a manifestation of Arctic Amplification, a process in which the Arctic experiences enhanced warming compared to lower latitudes due to multiple feedback mechanisms. A key contributor in the B–K region is Atlantification, driven by increased intrusion of warm Atlantic waters, which reduces sea ice cover, disrupts ocean stratification, and alters ocean-atmosphere fluxes52,53. As sea ice retreats, the lower albedo of open water enhances, reinforcing regional warming and sustaining feedback loops3. This intensified warming weakens the meridional temperature gradient and disrupts atmospheric circulation. Studies suggest it can alter the strength of the westerlies and shift storm tracks, potentially affecting circulation patterns like the NAO and the jet stream50.

Fig. 3
figure 3

Evolution of thermal and pressure anomalies during the spring season associated with low and high sea ice years spanning 1959–2021. Composite analysis of (a) sea surface temperature (SST), (b) 2-meter air temperature, and (c) sea level pressure (SLP) during low sea ice years. Panels (d), (e), and (f) show the corresponding anomalies for high sea ice years, respectively. Dotted lines in each panel represent the Barents–Kara region.

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Similar to SST, 2m air temperature anomalies exhibit a pronounced warming signal over the Arctic during low ice years (Fig. 3b). The strongest warming is concentrated over the B–K Sea, Laptev Sea, and Beaufort Sea, where temperature anomalies exceed 0.5°C. This atmospheric warming is primarily driven by the release of heat from the ice-free ocean surface into the lower troposphere, reinforcing the Arctic Amplification process. Additionally, warm anomalies extend beyond the Arctic, reaching northern Eurasia and parts of North America, highlighting the broader climatic implications of Arctic sea ice loss. This extensive warming can influence large-scale atmospheric circulation patterns, modulating the strength and position of the polar jet stream and mid-latitude weather systems. According to the thermodynamic principle, this warming leads to lower surface pressure because warm air is less dense than cold air, and as a result, it rises, creating a region of lower pressure at the surface. Hence, as evident from Fig. 3 (c), the Arctic region exhibits negative SLP anomalies during low ice years in summer, with the most pronounced anomalies observed in the B–K sea.

In contrast, during high sea ice years, cooler SST anomalies dominate the Arctic and adjacent high-latitude regions, with pronounced anomalies over the B–K sea, Laptev Sea, East Siberia Sea (Fig. 3d). Enhanced sea ice cover increases surface albedo, reducing solar heat absorption and limiting ocean-atmosphere heat exchange54. These cooler-than-average conditions result from reduced atmospheric heat transport, and a weakened pole-to-equator temperature gradient. Correspondingly, 2m air temperature anomalies (Fig. 3e) show widespread cooling, especially over regions of extensive sea ice. This reinforces feedback between sea ice, surface temperature, and circulation. The SLP anomalies (Fig. 3f) show high pressure over the Arctic and low pressure over the North Pacific, Norway, Sweden, and Finland. Together, panels 3d–f highlights the strong coupling between Arctic Sea ice variability and atmospheric circulation, with broader implications for high-latitude and tropical climate systems.

Further, the sensible heat flux (SHF) composites reveal strong upward anomalies along the Barents–Kara margins during high-ice years, while low-ice years are characterized by weaker and spatially diffuse anomalies (Fig. S3a, b). This contrast indicates that the magnitude of surface–atmosphere heat exchange depends strongly on the background sea ice state. SHF anomalies provide localized energy input into the lower troposphere, which can act as a potential precursor for planetary wave excitation and downstream circulations.

Fig. 4
figure 4

Evolution of geopotential height and wind speed anomalies during the spring season associated with low and high sea ice years in the upper (200 hPa) and lower (850 hPa) troposphere (a) Composite anomalies of geopotential height (shaded) and wind speed (vectors) at 200 hPa during low sea ice years (b) Same as (a) but for high sea ice years (c) Wind speed anomalies (shaded and vectors) at 200 hPa during low sea ice years (d) Composite anomalies of geopotential height (shaded) and wind speed (vectors) at 850 hPa during low sea ice years (e) Same as (d) but for high sea ice years (f) Wind speed anomalies (shaded and vectors) at 850 hPa during low sea ice years. The geopotential height anomalies reveal a ridge–trough–ridge–trough wave train structure in the 50°N–65°N latitudinal band, indicative of enhanced Rossby wave activity during low sea ice years. Note that ridges and troughs represent the high- and low-pressure systems, respectively.

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Atmospheric circulations associated with low and high ice years

The influence of Arctic sea ice variability on atmospheric circulation extends beyond thermal and pressure anomalies. These changes propagate into the upper and lower troposphere, altering geopotential height (GPH) at different pressure levels, which plays a crucial role in modulating large-scale atmospheric circulation, and subsequently the ISMR. To understand this connection, we examined composite anomalies of GPH and associated wind speed at 200 hPa and 850 hPa (Fig. 4). At 200 hPa, GPH anomalies reflect variations in the distribution of atmospheric mass and pressure in the upper troposphere, with ridges (high-pressure systems) and troughs (low-pressure systems) shaping large-scale circulation patterns. These features modulate the subtropical westerly jet stream (SWJ) and the tropical easterly jet (TEJ), both of which play a critical role in monsoonal dynamics.

The SWJ, located in the midlatitudes (Fig. S4a), is a fast-flowing, narrow air current in the upper troposphere that governs the movement of synoptic systems, including mid-latitude troughs and ridges55. A weakening of the SWJ is often associated with a delayed monsoon onset over India. In contrast, the TEJ is a dominant upper-level easterly wind system that emerges over the Indian Ocean, extends across South Asia during JJAS, and enhances upper-level divergence over the Indian subcontinent (Fig. S4a). It is established due to the meridional temperature gradient between the equatorial Indian Ocean and the Asian landmass, and extends from the Pacific Ocean to the west coast of the Sahel. Elevated heating over the Tibetan Plateau increases this gradient and leads to an enhancement of the TEJ56.

During low ice years (Fig. 4-Panel I), negative GPH anomalies (200 hPa) dominate the Arctic and extend downstream over Eurasia, indicating a weakened polar vortex and enhanced cyclonic circulation (Fig. 4a). This weakened vortex disrupts the typical jet streams, amplifying the Rossby wave activity, hence creating a ridge-trough-ridge-trough wave train pattern in the latitudinal band of 50°N-65°N. The deepened trough over central Eurasia not only influences the mid-tropospheric circulation but also interacts with the South Asian monsoon system. These GPH patterns are accompanied by the weakened and southward displaced subtropical jet stream. This enhances subsidence (sinking motion) over the Indo-Gangetic plain, which limits the strength of vertical air-motion needed for convection in this region. At 850 hPa, southwesterly winds are evident (Fig. S4b), which are moisture-laden winds that originate in the Indian Ocean and move towards the Indian subcontinent during the monsoon season. During low-ice years, upper-level winds (200 hPa) remain weak (Fig. 4c), while strengthened wind anomalies are prominent across the Indo-Gangetic Plain at 850 hPa (Fig. 4d). This atmospheric configuration influences precipitation patterns in the region through the interplay of moisture transport, atmospheric stability, and vertical motion dynamics43.

Enhanced winds at 850 hPa typically facilitate increased moisture transport into the region, supporting convective activity (Fig. 4f). However, when upper-level winds at 200 hPa are weak, they can suppress vertical air movement by promoting subsidence (sinking motion)57. This results in insufficient upper-level divergence, which is crucial for sustaining upward motion and deep convection. The weakened upper-level winds indicate reduced outflow from convective systems, limiting the development of intense precipitation events. The imbalance between lower-level moisture advection and upper-level stability creates a more stable atmosphere, inhibiting deep convective cloud formation and leading to a reduction in rainfall over the Indo-Gangetic Plain58. In contrast, during high-ice years, the atmospheric circulation exhibits an opposite configuration, thereby reversing the suppressed convection and supporting more favorable conditions for monsoonal rainfall across the Indo-Gangetic Plain (Fig. 4b, e).

Diagnostic analysis of regional atmospheric dynamics associated with Arctic sea ice variability

The variability in Arctic sea ice has the potential to modulate the ISMR through complex atmospheric processes involving changes in vertical motion, horizontal divergence and atmospheric instability. In this section, we present a diagnostic analysis of atmospheric conditions during contrasting sea ice states (low vs. high sea ice years) emphasizes key atmospheric variables that could mediate the Arctic to-topical linkages particularly over the Indo-Gangetic plain.

We first examined vertical atmospheric motion through a composite analysis of vertical velocity at 500 hPa level positioned between 200 hPa and 850 hPa levels during the monsoon season JJAS (Fig. 5a, d). During low ice year, significant positive vertical velocity anomalies (enhanced subsidence) are observed over the Indo-Gangetic Plain (Fig. 5a), indicating that the atmosphere in the region is experiencing stronger-than-usual subsidence relative to the climatological baseline. Enhanced subsidence (marked by positive anomalies) inhibits convective uplift by stabilizing the atmospheric column, leading to warming and drying of descending air parcels, thus suppressing cloud formation and precipitation43. Such a pattern aligns well with reduced rainfall observed during low sea ice years. Conversely, high sea ice years display weaker subsidence or even ascent (negative anomalies), supporting enhanced convective activity and increased rainfall.

Horizontal divergence anomalies at 500 hpa during the JJAS season further support this interpretation (Fig. 5b, e). During the low sea ice years, positive divergence anomalies (blue) over the Indo-Gangetic Plain (Fig. 5b) indicate the dominance of the horizontal outflow at mid-levels, suppressing vertical uplift and convection47. These anomalies are particularly pronounced during low ice years, further supporting the role of subsidence in reducing rainfall. In contrast, high ice years (Fig. 5e) are characterized by negative divergence anomalies (convergence) which enhance vertical motion, fostering convective cloud development and thus promoting increased rainfall. This contrasting divergence behavior underscores the critical role of horizontal airflow dynamics in modulating regional monsoonal rainfall in response to Arctic variability.

Fig. 5
figure 5

Diagnostic indicators of regional atmospheric dynamics during the summer season associated with low and high sea ice years over the region 22°–36° N; 78°–97° E. Composite analysis of (a) vertical velocity (b) divergence, and (c) Convective Available Potential Energy (CAPE) anomalies at 500 hPa during low ice years. Panels (d), (e), and (f) show the corresponding anomalies for high sea ice years.

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Additionally, atmospheric instability, quantified by CAPE, exhibits clear differentiation between low and high sea ice conditions (Fig. 5c, f). CAPE quantifies the buoyancy and potential instability of atmospheric parcels59,60. During low-ice years, significant negative CAPE anomalies are observed over the Indo-Gangetic Plain (Fig. 5c), suggests that the atmosphere is more stable, with cooler air parcels that are less buoyant and unable to rise effectively. This increased stability inhibits convection, leading to reduced cloud formation and precipitation. Conversely, positive CAPE anomalies during high ice years (Fig. 5f) indicate greater atmospheric instability, which facilitates the development of convective clouds and enhances the likelihood of precipitation. This pattern further reinforces the connection between atmospheric stability and rainfall patterns, with low ice years linked to drier conditions and high ice years associated with enhanced rainfall over the Indo-Gangetic Plain.

Collectively, these diagnostic atmospheric variables (vertical velocity, divergence, and CAPE) illustrate distinct regional dynamics during contrasting sea ice scenarios. Although this analysis identifies key atmospheric conditions associated with sea ice anomalies, further research is needed to elucidate specific dynamical pathways or intrinsic modulators—such as changes in planetary wave activity, mid-latitude jet-stream dynamics, or land-atmosphere interactions—that physically mediate Arctic-to-ISMR teleconnections.

Discussion

This study demonstrates a pronounced relationship between springtime B–K sea ice variability and ISMR, thereby emphasizing the far-reaching teleconnections that couple high-latitude processes with tropical climate systems. During low sea ice years, reduced monsoon activity is observed across key agricultural regions such as the Indo-Gangetic Plain, primarily due to the weakening of the monsoon trough and reduced land-sea thermal contrast. Conversely, high sea ice years depict an enhanced monsoon, particularly over the Indo-Gangetic Plain, driven by the strengthening of the monsoon trough and the low-level jet. This stark contrast in ISMR patterns emphasizes the influence of Arctic conditions on the tropics, further confirming the relevance of high-latitude processes in shaping tropical weather systems, a relationship that has been underexplored in regional studies61.

Previous studies have examined the teleconnections between Arctic sea ice variability and monsoon dynamics, primarily focusing on East Asian climate patterns. Yang et al. demonstrated that reduced sea ice in the B–K Seas during early winter influences June precipitation in China through atmospheric wave propagation62. Similarly, Xu et al. found that sea ice decline in the B–K region deepens the East Asian trough, affecting wintertime precipitation patterns63. While these studies highlight the significant role of Arctic forcing on East Asian climate variability, the impact on the ISMR has been less extensively investigated. Our study fills this research gap by establishing a robust relationship between B–K sea ice variability and the ISMR, providing novel insights into Arctic-tropical climate interactions that influence monsoon variability over the Indian subcontinent.

The modulation of atmospheric circulation patterns by Arctic sea ice further underscores the intricate connections between polar and tropical climates, reinforcing the importance of high-latitude processes in regulating tropical weather systems. Studies have highlighted that Arctic warming, associated with sea ice decline, disrupts mid-latitude atmospheric circulation by weakening the westerlies and shifting storm tracks3,64. Our analysis supports these findings, demonstrating that Arctic warming during low ice years induces lower surface pressures across the Arctic, which subsequently modifies global atmospheric circulation. These changes manifest in geopotential height (GPH) anomalies, where negative GPH anomalies over Eurasia during low ice years correspond to a weakened polar vortex and enhanced cyclonic circulation. This disruption in mid-latitude circulation is consistent with previous findings2, who linked Arctic Amplification to altered jet stream patterns and increased climate variability in the Northern Hemisphere.

In support of this, the streamfunction anomalies (Fig. 6a, b) reveal negative and positive anomalies over the B–K region during low and high ice years, respectively. This indicates contrasting wave propagation pathways downstream into Eurasia, and further influencing the large-scale circulation relevant for monsoon variability. As evident in Fig. 6c and d, wintertime polar vortex has a stronger tendency to weaken in years when Arctic sea ice is accelerating its disappearance, suggesting that the stratosphere-troposphere coupling provides an additional pathway through which the Arctic influences the tropical weather patterns and contributes to increased monsoonal variability.

Fig. 6
figure 6

Evolution of spring stream function anomaly and winter polar vortex associated with low and high ice years spanning from 1959–2021. Composite analysis of (a) stream function anomaly (b) zonal wind anomaly at 10 hPa during low ice years. Panels (c) and (d) show the corresponding anomalies for high ice years, respectively. Dotted lines in each panel represent the Barents–Kara region.

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Furthermore, our study highlights the role of large-scale atmospheric features such as the monsoon trough and the subtropical westerly jet (STWJ) in mediating the Arctic-ISMR connection. Previous research emphasized the importance of upper-level circulation anomalies in driving monsoon variability, noting that weakened STWJ patterns can lead to reduced convective activity and suppressed rainfall65. Our findings align with this perspective, as we observe weakened wind speeds at 200 hPa during low ice years, limiting vertical motion and inhibiting convection, ultimately contributing to monsoon suppression over the Indo-Gangetic Plain. Complementary diagnostics, including vertical velocity, divergence, and CAPE, reinforce this picture: negative anomalies and increased subsidence over the Indo-Gangetic Plain correspond to suppressed convection and reduced rainfall.

To provide a conceptual framework integrating these diagnostic observations, Fig. 7 offers a schematic illustration summarizing the plausible mechanisms connecting Arctic sea ice anomalies to ISMR variability. Arctic sea ice melt, especially in the Barents–Kara sector, induces enhanced ocean-atmosphere heat exchange and Arctic Amplification, associated with the weakening of winter polar vortex and altering mid-latitude jet streams4,32. The downstream impact of these atmospheric circulation changes may manifest as increased subsidence and atmospheric stability over the Indo-Gangetic Plain, leading to suppressed convection and diminished ISMR during low sea ice years. Conversely, reduced subsidence and increased instability during high sea ice years facilitate convective activity, enhancing monsoonal precipitation.

Despite these insights, there is a scope for further advancements First, the analysis primarily relies on reanalysis datasets, which may have inherent biases in capturing Arctic-tropical teleconnections. Future work using high-resolution regional climate models or coupled ocean-atmosphere models may help refine these findings. Second, while the study establishes a relationship between Arctic sea ice variability and ISMR, causality remains a challenge due to the complexity of atmospheric interactions. More detailed process-based modeling experiments, such as sensitivity simulations with perturbed sea ice conditions, could help clarify the mechanistic pathways of this teleconnection. Third, other external drivers, such as ENSO, IOD, and stratospheric dynamics, may also modulate the Arctic-ISMR relationship, warranting further investigation into their combined effects.

Nevertheless, this analysis underscores the interconnectedness of polar and tropical climate systems: changes in the Arctic reverberate across latitudes, altering circulation patterns and rainfall over South Asia. Given the central role of the ISMR in sustaining agriculture and food security in India, these findings have practical implications for seasonal prediction and climate risk assessment. By establishing a stronger empirical basis for the Arctic–ISMR teleconnection, this study contributes to a more integrated understanding of global climate linkages and highlights the importance of monitoring Arctic variability for anticipating tropical climate impacts.

Fig. 7
figure 7

Conceptual schematic illustrating the teleconnection between Arctic sea ice melt and the Indian Summer Monsoon Rainfall. The left side of the figure depicts a sequence of processes initiated by Arctic warming and sea ice melt. The retreat of Arctic sea ice, particularly in the Barents–Kara sector, is accompanied by Atlantification—intrusion of warmer Atlantic waters into the Arctic Ocean—which enhances local ocean-atmosphere heat exchange. This process contributes to Arctic Amplification characterized by a faster rate of warming in the Arctic compared to the global average. The amplified Arctic warming is associated with the weakening of the winter polar vortex and modifications in planetary wave activity, which in turn result in the enhanced meandering of the mid-latitude jet streams. These altered jet streams propagate downstream and impact the lower latitudes, including South Asia. The right side of the figure zooms into India, where the downstream impact manifests as subsidence (downward air motion) over the Indo-Gangetic region (oval shape marked in black dotted lines). This atmospheric subsidence is associated with reduced vertical motion and diminished convective activity, leading to less buoyant air parcels and thereby suppressing rainfall in this region. Collectively, the schematic highlights a potential diagnostic pathway through which Arctic sea ice variability may influence the strength and spatial variability of the ISMR.

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Conclusion

Our analysis demonstrates a robust linkage between Arctic Sea ice variability and the ISMR. Composite analyses spanning six decades reveal that low sea ice years are predominantly associated with suppressed rainfall over the Indo-Gangetic Plain, driven by weakened monsoon troughs, reduced vertical motion, and enhanced subsidence. Conversely, high sea ice years correspond to enhanced monsoon activity and rainfall in this critical region, albeit with distinct spatial heterogeneity that reflects the influence of localized convective processes. Moreover, our investigation of near-surface thermal and pressure fields, streamfunction anomaly, and winter polar vortex along with tropospheric variables such as geopotential height and wind speed at multiple atmospheric levels, illustrates that Arctic Sea ice changes initiate a cascade of atmospheric responses. These responses modulate moisture transport, convective activity, and large-scale circulation patterns, thereby establishing complex teleconnections between the polar regions and tropical monsoon systems. The interplay among altered sea surface temperatures, 2 m air temperatures, sea-level pressures, and upper- and lower-tropospheric circulation features underscores the intricate coupling between Arctic forcing and monsoonal dynamics. In summary, these findings highlight the critical need to incorporate Arctic variability into predictive models for the ISMR. By elucidating the diagnostic pathways linking high-latitude processes to tropical climate variability, our study provides valuable insights for improving monsoon forecasting and developing effective adaptation strategies for one of the world’s most vital climatic phenomena. Future research should further explore the interactions with other climate drivers such as ENSO and IOD to fully unravel the complexity of these global teleconnections.