Forests are integral to the global carbon cycle, acting as major carbon sinks, though their capacity can be altered by climate and land use changes. India has a diverse forest ecosystem, but the dynamics and changes in its carbon stocks under a changing climate are not well understood. This study investigates the regional and temporal changes in vegetation carbon biomass (VCB) within Indian forests by assessing changes across the recent past (1960-2020), near-term (2021-2040), mid-term (2041-2060), and long-term (2081-2100) using the LPJ-GUESS Ver.4.1.1 dynamic global vegetation model, forced with climate data from CMIP6 future climate projection. Our results indicate an overall rise in carbon stock across India's forests leading to a 35%, 62%, and 97% rise by 2100, under low, medium and high emissions, with the scenario trajectories diverging clearly by 2050. Desert and semi-arid regions have a substantial increase in forest VCB under high emissions, followed by the Trans-Himalaya, Gangetic Plain, Deccan Peninsula, and Northeast India in the long term (2081-2100), compared with historical simulations. Meanwhile, the Himalayas and Western Ghats have a comparatively lower increase. VCB trends are positively associated with temperature and precipitation, intensifying after 2040. Climate sensitivity and Granger causality analyses highlight that precipitation variability has a stronger national-scale impact on VCB, while temperature effects vary by region. The precipitation influence typically lags by similar to 2 years under low-medium emissions and by similar to 4 years under high emissions, while temperature shows a more consistent similar to 2 year lag. This study underscores the need for regionally tailored climate strategies and offers insights to guide future climate action, mitigation planning, and environmental sustainability.

This article provides an update on observed and projected climate change in India. India’s average temperature has risen by approximately 0.89°C during 2015–2024 relative to 1901–1930. Models project additional warming of about 1.2-1.3°C over India by mid-century under SSP2-4.5 (relative to the recent past (1995–2014)). The tropical Indian Ocean has warmed at 0.12°C per decade since 1950 and is projected to warm at 0.17°C per decade through 2100 under SSP2-4.5. Marine heatwave days are projected to rise from about 20 days per year historically (1970–2000) to nearly 200 days per year by mid-century. Mean southwest monsoon rainfall has declined by 0.5-1.5 mm day -1 every decade over the Indo-Gangetic plains and northeast India during 1951–2024. Extreme precipitation events have also intensified, with coastal Gujarat experiencing about 0.15 additional extreme events every decade during 1951–2024. CMIP6 models project about 6–8% increase in all-India mean southwest monsoon rainfall by mid-century relative to the recent past, though with high spatial variability. The Hindu Kush Himalaya have witnessed accelerated warming of about 0.28°C per decade (1950–2020); glacier mass losses accelerated from -0.17 m water equivalent (w.e.) yr -1 (2000–2009) to -0.28 m w.e. yr -1 (2010–2019), and models indicate a 30–50% reduction in glacier volume by 2100 at 1.5-2°C global warming levels. In the Arabian Sea, maximum pre-monsoon cyclone intensity has increased by 40% over 1982–2019. Sea levels in the north Indian Ocean have risen at 3.3 mm year -1 (1993–2017), with extreme sea level events increasing 2–3 fold. Historical one-in-hundred-year extreme sea level events along the Arabian Sea coastline are projected to become annual occurrences by mid-century under SSP2-4.5. We also report increasing trends in compound hot-dry extremes in parts of India. Our findings highlight spatially differentiated hotspots of climate change across India and provide policy-relevant insights.

The Indo-Pacific region, a critical economic and geopolitical hub, faces intensifying climate risks, including accelerating sea-level rise, extreme weather events—particularly heatwaves amplified by rapid urbanization—and glacial retreat in the Hindu Kush Himalayas. While advancements in climate science have significantly improved future climate projections, gaps remain in translating this knowledge into actionable adaptation strategies. Barriers such as data inaccessibility, weak institutional and international coordination, and financial constraints hinder effective climate action. This study synthesizes existing climate knowledge for the Indo-Pacific region, emphasizing the need for localized, community-driven adaptation approaches. Key challenges include the vulnerability and exposure of coastal communities to sea-level rise, the limitations of current urban-scale climate modeling, and the underrepresentation of sociocultural factors in climate adaptation strategies. The integration of Artificial Intelligence (AI) and machine learning (ML) in climate models presents an opportunity to enhance urban climate resilience, while the incorporation of indigenous knowledge rooted in scientific principles offers a critical pathway to improving localized adaptation efforts. Additionally, science communication plays a pivotal role in ensuring that climate research reaches policymakers and communities in an accessible and actionable manner. We advocate for a paradigm shift from a linear value chain to a value cycle approach, where scientific insights inform policy and local contexts inform research priorities. By bridging climate science, policy, and communities through regional platforms such as the Indo-Pacific My Climate Risk Hub at the Indian Institute of Tropical Meteorology (IITM) Pune, India, this paper outlines pathways for collaborative climate action. This work proposes actionable strategies for regional resilience.

Anthropogenic climate change has led to rapid and widespread changes in the atmosphere, land, ocean, cryosphere, and biosphere, leading to more pronounced weather and climate extremes globally. Recent IPCC reports have highlighted that the probability of compound extreme events, which can amplify risk, has risen in multiple regions. However, significant gaps remain in our understanding of the drivers and mechanisms behind these events. This concept paper discusses compound events in the Asian region in the context of its unique and diverse geographical settings, and regional climatic features including the seasonal monsoons. Notably, Asia is the world’s most disaster-affected region due to weather, climate, and water-related hazards. Therefore, an integrated understanding of how climate change will impact compound events in this region is essential for effective forewarning and risk mitigation. This paper analyzes three typologies of compound events in the Asian region, illustrating their regional complexity and potential linkages to climate change. The first typology pertains to compound floods, for example, the devastating floods in the Indus River Basin and adjoining Western Himalayas during 2022 caused by the combined effects of heavy monsoon rainfall, intense pre-monsoon heatwaves, glacier melt, and modes of climate variability. The second typology relates to compound heatwave-drought events that have prominently manifested in East and South Asia, and are linked to large-scale drivers of the land-atmosphere–ocean coupled system and local feedbacks. The third typology relates to marine extremes involving the compounding effects of ocean warming, sea-level rise, marine heatwaves, and intensifying tropical cyclones. We identify key knowledge gaps in understanding and predicting compound events over the Asian region and discuss advances required in science and technology to address these gaps. We also provide recommendations for the effective utilization of climate information towards improving early warning systems and disaster risk reduction.

Global climate is regulated by the ocean, which stores, releases, and transports large amounts of mass, heat, carbon, and oxygen. Understanding, monitoring, and predicting the exchanges of these quantities across the ocean’s surface, their interactions with the atmosphere, and their horizontal and vertical pathways through the global oceans, are key for advancing fundamental knowledge and improving forecasts and longer-term projections of climate, weather, and ocean ecosystems. The existing global observing system provides immense value for science and society in this regard by supplying the data essential for these advancements. The tropical ocean observing system in particular has been developed over decades, motivated in large part by the far-reaching and complex global impacts of tropical climate variability and change. However, changes in observing needs and priorities, new challenges associated with climate change, and advances in observing technologies demand periodic evaluations to ensure that stakeholders’ needs are met. Previous reviews and assessments of the tropical observing system have focused separately on individual basins and their associated observing needs. Here we provide a broader perspective covering the tropical observing system as a whole. Common gaps, needs, and recommendations are identified, and interbasin differences driven by socioeconomic disparities are discussed, building on the concept of an integrated pantropical observing system. Finally, recommendations for improved observations of tropical basin interactions, through oceanic and atmospheric pathways, are presented, emphasizing the benefits that can be achieved through closer interbasin coordination and international partnerships.

Tropical sea surface temperature warming displays distinct regional patterns, but its influence on Madden-Julian Oscillation propagation remains unclear. Here we investigate how a shift toward La Ni & ntilde;a-like conditions around 1999 has affected Madden-Julian Oscillation propagation by comparing two periods: 1979-1998 and 2003-2022. Using satellite observations and reanalysis data, we show that propagation accelerated over the Indian Ocean and the Maritime Continent but slowed over the western Pacific in the later period. These contrasting responses are linked to changes in sea surface temperature patterns and associated atmospheric conditions. Over the Indian Ocean, enhanced low-level horizontal moisture gradients and increased upper-tropospheric stability facilitate propagation, while weakened moisture gradient and suppressed convection hinder it over the western Pacific. Despite complex terrain and atmospheric interactions over the Maritime Continent, background changes still favor enhanced propagation. These findings highlight the sensitivity of Madden-Julian Oscillation behavior to regional ocean-atmospheric conditions, with implications for improving subseasonal prediction.

The global burden of dengue disease is escalating under the influence of climate change, with India contributing a third of the total. The non-linearity and regional heterogeneity inherent in the climate-dengue relationship and the lack of consistent data makes it difficult to make useful predictions for effective disease prevention. The current study investigates these non-linear climate-dengue links in Pune, a dengue hotspot region in India with a monsoonal climate and presents a model framework for predicting both the near-term and future dengue mortalities. Dengue mortality and meteorological conditions over a twelve-year period (2004–2015) are analyzed using statistical tools and machine learning methods. Our findings point to a significant influence of temperature, rainfall, and relative humidity on dengue mortality in Pune, at a time-lag of 2–5 months, providing sufficient lead time for an early warning targeted at curbing dengue outbreaks. We find that moderate rains spread over the summer monsoon season lead to an increase in dengue mortality, whereas heavy rains reduce it through the flushing effect, indicating the links between dengue and monsoon intraseasonal variability. Additionally, warm temperatures above 27°C and humidity levels between 60% and 78% elevate the risk of dengue. Based on these weather-dengue associations, we developed a machine-learning model utilizing the random forest regression algorithm. The dengue model yields a skillful forecast, achieving a statistically significant correlation coefficient of r = 0.77 and a relatively low Normalized Root Mean Squared Error score of 0.52 between actual and predicted dengue mortalities, at a lead time of two months. The model finds that the relative contributions of temperature, rainfall, and relative humidity to dengue mortality in Pune are 41%, 39%, and 20%, respectively. We use the dengue model in conjunction with the climate change simulations from the Coupled Model Intercomparison Project phase 6 for the future dengue mortality projections under a global warming scenario. In a changing climate, dengue-related mortality in Pune is projected to rise by 13% in the near future (2021–2040), 23–40% in the mid-century (2041–2060), and 30–112% in the late century (2081–2100) under low-to-high emission pathways in response to the associated increase in temperature and changes in monsoon rainfall patterns.

On the basis of the synergistic seasonal evolution of sea surface salinity over the Maritime Continent (MC SSS) and South Asian rainfall, the singular value decomposition analysis is used to identify a robust coupled mode between springtime MC SSS and South Asian summer monsoon rainfall (SASMR). High MC SSS is accompanied by a SASMR dipole (suppressed rainfall in northern India and enhanced rainfall in the eastern Tibetan Plateau), which is largely caused by the negative and positive moisture flux convergence associated with ocean-to-land moisture transport. In addition, the negative rainfall anomalies in northern India decrease evapotranspiration and further suppress Indian summer monsoon rainfall (ISMR) because of land-to-atmosphere feedback. The seasonal forecast of ISMR can be improved by incorporating SSS into a physics-based empirical model, which was selected according to adjusted R2 values, Mallows' Cp values, and cross-validation. In this study, we found that a new predictor (ocean salinity) over the MC region can improve ISMR prediction, which highlights the importance of enhanced monitoring of salinity in the MC region.

Abstract The eastward‐moving large‐scale convective system associated with the Madden‐Julian Oscillation (MJO) significantly impact global weather and climate. Recent decades have seen notable changes in the MJO's lifecycle due to non‐uniform tropical ocean warming, with the roles of natural climate variability and anthropogenic influence still requiring quantification. This study examines observed and projected long‐term changes in the MJO phase speed using four twentieth‐century reanalyses and CMIP6 simulations. We find a substantial increase in MJO phase speed in three reanalyses during the twentieth century (0.6–1.2 m s⁻1 century⁻1) and further increase in MJO phase speed during the twenty‐first century (0.3–1.5 m s⁻1 century⁻1), with notable multidecadal fluctuations. We attribute the overall acceleration of the MJO to the global warming‐driven increase in the meridional moisture gradient around the warm pool while attributing the multidecadal variability in the MJO phase speed to changes in the zonal moisture gradient associated with the Pacific Decadal Oscillation.

Massive river interlinking projects are proposed to offset observed increasing droughts and floods in India, the most populated country in the world. These projects involve water transfer from surplus to deficit river basins through reservoirs and canals without an in-depth understanding of the hydro-meteorological consequences. Here, we use causal delineation techniques, a coupled regional climate model, and multiple reanalysis datasets, and show that land-atmosphere feedbacks generate causal pathways between river basins in India. We further find that increased irrigation from the transferred water reduces mean rainfall in September by up to 12% in already water-stressed regions of India. We observe more drying in La Niña years compared to El Niño years. Reduced September precipitation can dry rivers post-monsoon, augmenting water stress across the country and rendering interlinking dysfunctional. Our findings highlight the need for model-guided impact assessment studies of large-scale hydrological projects across the globe.

Observing and understanding the state of the Indian Ocean and its influence on climate and maritime resources is of critical importance to the populous nations that rim its border. Acute gaps have occurred in the Indian Ocean Observing System, which underpins monitoring and forecasting of regional climate, since the start of the COVID pandemic. The pandemic disrupted the deployment and maintenance cruises for the observational array and also resulted in supply chain issues for procurement and refurbishment of equipment. In particular, the observational platforms that provide key measurements of upper ocean heat variability have experienced serious multiyear declines. There is now record-low data reporting and the platforms that are successfully reporting are old and quickly surpassing their expected period of reliable operation. The overall impact on the observing system will take a few years to fully comprehend. In the meantime, there is a critical need to document the gaps that have appeared over the past few years and how this will impact our ability to improve understanding and model representations of the real world that support regional weather and climate forecasts. The article outlines the expected slow road to recovery for the Indian Ocean Observing System, documents case studies of successful international collaborative efforts that will revive the observing system and provides guidelines for resilience from unexpected external factors in the future.

AbstractMarine heatwaves have profoundly impacted marine ecosystems over large areas of the world oceans, calling for improved understanding of their dynamics and predictability. Here, we critically review the recent substantial advances in this active area of research, including the exploration of the three-dimensional structure and evolution of these extremes, their drivers, their connection with other extremes in the ocean and over land, future projections, and assessment of their predictability and current prediction skill. To make progress on predicting and projecting marine heatwaves and their impacts, a more complete mechanistic understanding of these extremes over the full ocean depth and at the relevant spatial and temporal scales is needed, together with models that can realistically capture the leading mechanisms at those scales. Sustained observing systems, as well as measuring platforms that can be rapidly deployed, are essential to achieve comprehensive event characterizations while also chronicling the evolving nature of these extremes and their impacts in our changing climate.

Bangladesh and northeast India are the most densely populated regions in the world where severe floods as a result of extreme rainfall events kill hundreds of people and cause socio‐economic losses regularly. Owing to local high topography, the moisture‐carrying monsoon winds converge near southeast Bangladesh (SEB) and northeast Bangladesh and India (NEBI), which produces significant extreme rainfall events from May to October. Using observed data, we find an increasing trend of 1‐day extreme event (150 mmday) frequency during 1950–2021. The extreme rainfall events quadrupled over western Meghalaya (affecting NEBI) and coastal SEB during this period. Composite analysis indicates that warm Bay of Bengal sea‐surface temperature intensifies the lower tropospheric moisture transport and flux through the low‐level jet (LLJ) to inland, where mountain‐forced moisture converges and precipitates as rainfall during extreme events. To understand the role of climate change, we use high‐resolution downscaled models from Coupled Model Intercomparison Project phase 6 (CMIP6). We find that the monsoon extreme event increase is ongoing and the region of quadrupled events further extends over the NEBI and SEB in the future (2050–2079) compared with historical simulations (1950–1979). A quadrupling of the intense daily moisture transport episodes due to increased LLJ instability, a northward shift of LLJ, and increased moisture contribute to the increased future extreme events. This dynamic process causes moisture to be transported to the NEBI from the southern Bay of Bengal, and the local thermodynamic response to climate change contributes to the increased extreme rainfall events. The CMIP6 projection indicates that more devastating flood‐causing extreme rainfall events will become more frequent in the future.

During the summer of 2016, the northern East China Sea and the southern Yellow Sea (NECS-SYS) experienced one of the most severe and devastating marine heatwaves (MHWs) on record, with a temperature anomaly exceeding 4 degrees C. This shallow semi-enclosed continental shelf region is widely recognized as a significant hotspot for MHWs with associated incidences of harmful algae blooms. Previous studies have highlighted the importance of mixed layer shoaling as a crucial factor in the genesis of MHWs in the global ocean. The current study employed the Hybrid Coordinate Ocean Model reanalysis data set during 1994-2015 to delve into the mechanisms driving mixed layer shoaling during NECS-SYS MHW genesis. Our findings reveal the significant role of the northward propagating boreal summer intraseasonal oscillation in promoting MHW genesis and intensification. Specifically, boreal summer intraseasonal oscillation phases 5, 6, and 7 contribute to the favorable conditions that facilitate MHW formation by inducing mixed layer shoaling and increasing solar influx, with mixed layer shoaling playing a more dominant role. The current study provides insights into the relative influences of wind, salinity, and temperature on mixed layer shoaling. We observe that wind plays the most significant role in mixed layer shoaling, followed by temperature and salinity. The boreal summer intraseasonal oscillation induced wind relaxation, increased shortwave radiation, and freshwater influx lead sea surface temperature by 7, 5, and 4 days, respectively. Importantly, mixed layer shoaling leads SST anomalies by 1-2 days. Therefore, the current study also suggests an intraseasonal predictability source for NECS-SYS MHWs. The northern East China Sea and southern Yellow Sea (NECS-SYS) experienced one of the most severe and devastating marine heatwaves (MHWs) during the summer of 2016. The region, situated in the northwest Pacific, is characterized by a shallow semi-enclosed marginal sea, and receives a significant amount of freshwater influx from the Yangtze River. Previous studies have demonstrated that shoaling of surface mixed layer is a primary factor contributing to the genesis of MHWs in the global ocean. The present study emphasizes the role of the boreal summer intraseasonal oscillation in creating favorable conditions for MHWs genesis in these regions. Our research demonstrates that boreal summer intraseasonal oscillation phases 5, 6, and 7 contribute to the amplification of solar flux and the shoaling of mixed layer by reducing wind speed and introducing freshwater in this region. We provide a comprehensive understanding of the individual influences of wind, salinity, and temperature on mixed layer shoaling, highlighting the significance of mixed layer shoaling as a fundamental driver of MHWs. Moreover, our research also directs to an intraseasonal predictability source of the NECS-SYS MHWs. Boreal Summer Intraseasonal Oscillation phases 5, 6, and 7 facilitate marine heatwaves (MHWs) in the northern East China Sea and southern Yellow Sea Mixed layer shoaling is a more critical factor than the enhanced shortwave for MHW genesis and intensification in this region Occurrences of MHWs in phase with boreal summer intraseasonal oscillation offer subseasonal predictability of MHWs

Our understanding of surface ocean and lower atmosphere processes in the Indian Ocean (IO) region shows significant knowledge gaps mainly due to the paucity of observational studies. The IO basin is bordered by landmasses and an archipelago on 3 sides with more than one-quarter of the global population dwelling along these coastal regions. Therefore, interactions between dynamical and biogeochemical processes at the ocean–atmosphere interface and human activities are of particular importance here. Quantifying the impacts of changing oceanic and atmospheric processes on the marine biogeochemical cycle, atmospheric chemistry, ecosystems, and extreme events poses a great challenge. A comprehensive understanding of the links between major physical, chemical, and biogeochemical processes in this region is crucial for assessing and predicting local changes and large-scale impacts. The IO is one of the SOLAS (Surface Ocean-Lower Atmosphere Study) cross-cutting themes as summarized in its implementation strategy. This article attempts to compile new scientific results over the past decade focusing on SOLAS relevant processes within the IO. Key findings with respect to monsoon and air–sea interactions, oxygen minimum zones, ocean biogeochemistry, atmospheric composition, upper ocean ecosystem, and interactions between these components are discussed. Relevant knowledge gaps are highlighted, with a goal to assist the development of future IO research programs. Furthermore, we provided several recommendations to conduct interdisciplinary research to advance our understanding on the land–ocean–atmospheric interaction in the IO.

Climate change has emerged across many regions. Some observed regional climate changes, such as amplified Arctic warming and land-sea warming contrasts have been predicted by climate models. However, many other observed regional changes, such as changes in tropical sea surface temperature and monsoon rainfall are not well simulated by climate model ensembles even when taking into account natural internal variability and structural uncertainties in the response of models to anthropogenic radiative forcing. This suggests climate model predictions may not fully reflect what our future will look like. The discrepancies between models and observations are not well understood due to several real and apparent puzzles and limitations such as the “signal-to-noise paradox” and real-world record-shattering extremes falling outside of the possible range predicted by models. Addressing these discrepancies, puzzles and limitations is essential, because understanding and reliably predicting regional climate change is necessary in order to communicate effectively about the underlying drivers of change, provide reliable information to stakeholders, enable societies to adapt, and increase resilience and reduce vulnerability. The challenges of achieving this are greater in the Global South, especially because of the lack of observational data over long time periods and a lack of scientific focus on Global South climate change. To address discrepancies between observations and models, it is important to prioritize resources for understanding regional climate predictions and analyzing where and why models and observations disagree via testing hypotheses of drivers of biases using observations and models. Gaps in understanding can be discovered and filled by exploiting new tools, such as artificial intelligence/machine learning, high-resolution models, new modeling experiments in the model hierarchy, better quantification of forcing, and new observations. Conscious efforts are needed toward creating opportunities that allow regional experts, particularly those from the Global South, to take the lead in regional climate research. This includes co-learning in technical aspects of analyzing simulations and in the physics and dynamics of regional climate change. Finally, improved methods of regional climate communication are needed, which account for the underlying uncertainties, in order to provide reliable and actionable information to stakeholders and the media.