Hyperlocal urban extreme rainfall forecast system: A convolutional neural network-transfer learning-based downscaling approach - Puja Tripathy; Raghu Murtugudde; Vikram Singh Chandel; P.E. Zope; Subimal Ghosh (Urban Climate, 2026)

Regional dynamics of India’s monsoon driven by negative covariance and distinct moisture sources - Vikram S. Chandel; Raghu Murtugudde; Subimal Ghosh (Climate Dynamics, 2026)

Simulating forest fires in Uttarakhand: Uncovering the role of meteorological variables - Leena Khadke; Sandipan Mukherjee; Subimal Ghosh (Journal of Earth System Science, 2026)

Incorporating varying vegetation characteristics driven by Hydrometeorology in the land surface modeling by variable Infiltration Capacity model - Dawn Emil Sebastian; Subimal Ghosh (Journal of Hydrology, 2026)

Vapor pressure deficit dominates sap flow variability across forest biomes - Leena Khadke; Akash Verma; Sandipan Mukherjee; Subimal Ghosh (Journal of Hydrology, 2026)

Possible influences of Prosopis juliflora on groundwater resources in Gujarat - Krutika Tundia; Subimal Ghosh; Pennan Chinnasamy (Journal of Hydrology, 2025)

Increased carbon uptake by vegetation across ecosystems during post moisture pulse - Praveen Mutyala; Kiran Chand Thumaty; R. Suraj Reddy; Sampelli Anoop; Subimal Ghosh (Journal of Hydrology, 2025)

An integrated framework for flood risk forecasting utilizing global weather predictions and hydrodynamic Modelling: An appraisal of the Krishna River Basin case study, India - Ankan Chakraborty; Kaustav Mondal; Mousumi Ghosh; Subimal Ghosh; Subhankar Karmakar (Journal of Environmental Management, 2025)

Atmospheric aridity perturbs critical soil moisture thresholds of plant water stress over Indian biomes - Anoop Sampelli; Ajinkya Khandare; Ramana Muvva Venkata; Subhankar Karmakar; Subimal Ghosh (Advances in Water Resources, 2025)

North–South Asymmetry of Pre‐Monsoon Rainfall Over the Western Ghats in India - Vikram Singh Chandel; Tejasvi Chauhan; Kumar K. Iyer; Subimal Ghosh; Raghu Murtugudde (International Journal of Climatology, 2025)
The pre‐monsoon rains break a long dry spell over many regions of India, bringing relief from the summer heat, reducing air pollution, aiding agriculture and water resources. However, the pre‐monsoon rainfall shows a strong contrast along the Western Ghats, with the southern portion receiving higher and more consistent rainfall and the northern Ghats experiencing sporadic pre‐monsoon rains. Here, we examine the causes for this disparity using ERA‐5 reanalysis and observed precipitation from two stations, Mumbai and Kochi, in the northern and southern Western Ghats, respectively. We find that during the pre‐monsoon, south‐westerly wind bursts occur occasionally, bringing moisture to the southern Western Ghats, which build up moist convective instabilities, causing pre‐monsoon rainfall over Kochi. However, during the periods when it rains over Mumbai, these winds are stronger and shift northwards. With increased moisture loading and stronger winds, rainfall days over Mumbai are associated with increased moist static energy over the northern Western Ghats. We further show that the northward shift of the south‐westerly winds is forced by a Rossby wave propagation pattern in the upper troposphere, which causes a surface cyclonic circulation and warm sea surface temperatures over the northern Arabian Sea. Our findings demonstrate that the episodic northward expansion of south‐westerly winds over the Arabian Sea also spreads the rainfall northward over the Western Ghats during the pre‐monsoon. While our results shed light on the potential mechanisms, further studies are needed to understand the feedbacks driving this northward shift and its consequences for the monsoon season, if any. This is especially urgent since the northern Arabian Sea warming is now shown to affect extreme rainfall events over the northern Western Ghats, northwestern India and into Pakistan.

NLP-driven crowdsourcing for urban flood monitoring: insights from mumbai - Aniket Navalkar; Shrabani S. Tripathy; Mayank Gupta; Sanghita Basu; Puja Tripathy; Archismita Banerjee; Sheeba Sekharan; Raghu Murtugudde; Subimal Ghosh (Sustainable Cities and Society, 2025)

District to subdistrict scale optimum irrigation water management planning at multi-week lead time - Adrija Roy; A K Sahai; Subimal Ghosh (Journal of Earth System Science, 2025)

Micrometeorological drivers affecting the variability of CO2 uptake in the Himalayan Oak and Pine dominated ecosystems: An assessment of causal relationships - Leena Khadke; Sandipan Mukherjee; Subimal Ghosh (Journal of Hydrology, 2025)

Assessment of socio-economic strategies for managing regional flood risk in an urban coastal catchment - Mousumi Ghosh; Subimal Ghosh; Subhankar Karmakar (Urban Climate, 2024)

Improved water use efficiency of vegetation due to carbon fertilization not translating to increased soil moisture in India - Akash Verma; Subimal Ghosh (Journal of Hydrology, 2024)

Simulating urban surface energy balance of an academic campus and surroundings in Mumbai, India - Mayank Gupta; Raghu Murtugudde; Subimal Ghosh (Urban Climate, 2024)

Evaluating pulse-reserve characteristics of Soil-Plant continuum in India using remote sensing - Sampelli Anoop; Muvva Venkata Ramana; Subhankar Karmakar; Subimal Ghosh (Journal of Hydrology, 2024)

Analysis of Mumbai floods in recent years with crowdsourced data - Shrabani Sailaja Tripathy; Sautrik Chaudhuri; Raghu Murtugudde; Vedant Mhatre; Dulari Parmar; Manasi Pinto; P.E. Zope; Vishal Dixit; Subhankar Karmakar; Subimal Ghosh (Urban Climate, 2024)

Analytical framework based on thermodynamics to estimate spatially distributed surface energy fluxes from remotely sensed radiations - Mayank Gupta; Martin Wild; Subimal Ghosh (Remote Sensing of Environment, 2023)

Improving the subseasonal variability of the Indian summer monsoon in a climate model - Ankur Srivastava; Suryachandra A. Rao; Subimal Ghosh (International Journal of Climatology, 2023)
Many global climate models, including the Climate Forecast System version 2 (CFSv2), have a biased representation of subseasonal modes of variability of the Indian summer monsoon. For instance, they simulate a weaker summer mean monsoon low‐pressure systems (LPS) climatology, faster than observed northward propagation of monsoon intraseasonal oscillations (MISOs), and a systematic dry bias over Indian landmass. The Bay of Bengal (BoB), with its shallow mixed layers and unique thermal stratification, significantly modulates the convective activity in this region at subseasonal‐to‐seasonal timescales through modulation of sea surface temperature. The highly stratified upper ocean in the BoB is due to the enormous freshwater it receives from rains and rivers. A river routing model is coupled to the CFSv2 to account for the riverine freshwater and the improvements in modelling the upper‐ocean structure are analysed. Model simulations indicate that inclusion of temporally varying riverine freshwater improves the upper‐ocean state in the BoB and the observed mixed‐layer temperature gradients in the Bay are simulated reasonably after incorporating the time varying river runoff. This resulted in increased LPS lifetime and track density, and enhanced rainfall over central India. Better representation of the upper‐ocean stratification in the model leads to larger post‐convection shoaling of mixed layers at intraseasonal timescales, thereby forming thick barrier layers. Enhanced air–sea interactions restricted to the shallow mixed layer are associated with stronger vorticity, specific humidity and low‐level convergence to the north of the intraseasonal convection band. This enhanced low‐level moisture convergence north of the convection centre results in realistic northward propagation of MISO and aids LPS activity. It is demonstrated that better simulation of the upper‐ocean structure in coupled climate models can improve the representation of subseasonal modes of monsoon variability. These results bear important implications for operational forecasting.

Remote sensing and climate services improve irrigation water management at farm scale in Western-Central India - Adrija Roy; Raghu Murtugudde; Parag Narvekar; A.K. Sahai; Subimal Ghosh (Science of The Total Environment, 2023)

Aerosol-heavy precipitation relationship within monsoonal regimes in the Western Himalayas - Suma Bhanu Battula; Steven Siems; Arpita Mondal; Subimal Ghosh (Atmospheric Research, 2023)

Thinking about water and air to attain Sustainable Development Goals during times of COVID-19 Pandemic - Abhijit Mukherjee; S Suresh Babu; Subimal Ghosh (Journal of Earth System Science, 2020)

Revamping extended range forecast of Indian summer monsoon - Pushpendra Raghav; Swatah Snigdha Borkotoky; Jisha Joseph; Rajib Chattopadhyay; A. K. Sahai; Subimal Ghosh (Climate Dynamics, 2020)

Assessing the impacts of historical and future land use and climate change on the streamflow and sediment yield of a tropical mountainous river basin in South India - Rakesh Kumar Sinha; T. I. Eldho; Ghosh Subimal (Environmental Monitoring and Assessment, 2020)

Assessing the impacts of land cover and climate on runoff and sediment yield of a river basin - Rakesh Kumar Sinha; T. I. Eldho; Ghosh Subimal (Hydrological Sciences Journal, 2020)

Role of vertical velocity in improving finer scale statistical downscaling for projection of extreme precipitation - Aditya Gusain; H. Vittal; Shashikanth Kulkarni; Subimal Ghosh; Subhankar Karmakar (Theoretical and Applied Climatology, 2019)

Future urban rainfall projections considering the impacts of climate change and urbanization with statistical–dynamical integrated approach - Hiteshri Shastri; Subimal Ghosh; Supantha Paul; Hossein Shafizadeh-Moghadam; Marco Helbich; Subhankar Karmakar (Climate Dynamics, 2019)

A minimalistic seasonal prediction model for Indian monsoon based on spatial patterns of rainfall anomalies - Rishi Sahastrabuddhe; Subimal Ghosh; Anamitra Saha; Raghu Murtugudde (Climate Dynamics, 2019)

Performance of the CORDEX regional climate models in simulating offshore wind and wind potential - Sumeet Kulkarni; M. C. Deo; Subimal Ghosh (Theoretical and Applied Climatology, 2019)

Disentangling sea-surface temperature and anthropogenic aerosol influences on recent trends in South Asian monsoon rainfall - Nitin Patil; Chandra Venkataraman; Kaushik Muduchuru; Subimal Ghosh; Arpita Mondal (Climate Dynamics, 2019)

Understanding the role of moisture transport on the dry bias in indian monsoon simulations by CFSv2 - A. S. Sahana; Amey Pathak; M. K. Roxy; Subimal Ghosh (Climate Dynamics, 2019)

Regionalized Design Rainfall Estimation: an Appraisal of Inundation Mapping for Flood Management Under Data-Scarce Situations - Mohit Prakash Mohanty; Mazhuvanchery Avarachen Sherly; Subhankar Karmakar; Subimal Ghosh (Water Resources Management, 2018)

Future projections of Indian summer monsoon rainfall extremes over India with statistical downscaling and its consistency with observed characteristics - Kulkarni Shashikanth; Subimal Ghosh; Vittal H; Subhankar Karmakar (Climate Dynamics, 2018)

Multi-ensemble regional simulation of Indian monsoon during contrasting rainfall years: role of convective schemes and nested domain - Anjana Devanand; Subimal Ghosh; Supantha Paul; Subhankar Karmakar; Dev Niyogi (Climate Dynamics, 2018)

The Chennai extreme rainfall event in 2015: The Bay of Bengal connection - Alugula Boyaj; Karumuri Ashok; Subimal Ghosh; Anjana Devanand; Govardhan Dandu (Climate Dynamics, 2018)

Understanding the cascade of GCM and downscaling uncertainties in hydro‐climatic projections over India - Tarul Sharma; H. Vittal; Surbhi Chhabra; Kaustubh Salvi; Subimal Ghosh; Subhankar Karmakar (International Journal of Climatology, 2018)
India is a major agrarian country strongly impacted by spatio‐temporal variations in the Indian monsoon. The impact assessment is usually accomplished by implementing projections from general circulation models (GCMs). Unfortunately, these projections cannot capture the dynamicity of the monsoon and require either statistical (SD) or dynamical (DD) downscaling of the GCM projections to a finer resolution. Both downscaling techniques can capture the spatio‐temporal variation in climatic variables but are marred by uncertainty in the projections resulting from the choice of the GCM and downscaling method, which affects climate change adaptations. Here, we assessed uncertainties in the projections of hydro‐climatic variables over India by considering multiple downscaling techniques, multiple GCMs, and their combined effects (referred as the total uncertainty). Multiple hydrological variables were simulated by implementing the variable infiltration capacity model that considered outputs from DD (derived by the coordinated regional climate downscaling experiment, CORDEX) and SD forced with multiple GCM simulations. Our results showed that the SD projections captured the observed spatio‐temporal variability of hydro‐climatic variables more efficiently than the DD projections. Importantly, contribution from the downscaled projections to the total uncertainty was significantly smaller compared to the inter‐GCM uncertainty. We believe uncertainty analysis is an important component of good scientific practice; however, several researchers appear to be rather reluctant to embrace the concept of uncertainty in making projections, predictions, and forecasting. It remains a common practice to show climate change exercises to decision‐makers/stakeholders, without uncertainty bounds. Here, a successful attempt was made to identify the key sources of uncertainty and adequately bracket the uncertainty, indicating a requirement of the code of practice to provide formal guidance, particularly for climate‐change impact assessments. This consequently emphasized the importance of follow‐up research to understand the inter‐GCM uncertainty, which has a significant impact on sustainable agriculture and water resources management in India.

Can statistical downscaling improve consensus among CMIP5 models for Indian summer monsoon rainfall projections? - C. G. Madhusoodhanan; K. Shashikanth; T. I. Eldho; Subimal Ghosh (International Journal of Climatology, 2018)
The projections of plausible changes in future climate using coarse scale General Circulation Models (GCMs) for non‐smooth climate variables, such as precipitation, possess limited skill and are often conflicting. The confidence in precipitation projections is further obscured due to the exclusion of inter‐model and natural internal variability. The present study investigates how far statistical downscaling can provide confidence in future projections while incorporating these major uncertainties under a moderate radiative forcing scenario. Here, we assess the consensus for future changes in Indian Summer Monsoon Rainfall (ISMR) from 20 CMIP5 GCMs and their statistically downscaled counterparts at 0.05° resolution for the 21st century. The Statistical Downscaling (SD) model is skillful in capturing the spatial variability of observed ISMR and shows significant improvement over host GCMs. GCMs show consistent but spatially inhomogeneous increase in future ISMR which intensify towards the end of the century. On the other hand, the downscaled outputs show spatially non‐uniform trends which intensify in their respective directions from near to long term. Both projections show high inter‐model inconsistencies. The multi‐model consensus among GCMs and SD across these in‐congruent models depicts inconclusive and highly uncertain changes in future. The GCMs show significant change with high model inconsistency uniformly across India across time scales. Even though the downscaled outputs show similar results for majority of the Indian landmass, it is highly heterogeneous across time horizons. There is also an emergence of medium evidence for future changes in ISMR for few regions of the country such as the southern Western Ghats, foothills of the Himalaya and central India. The study brings out that even‐improved simulations from downscaling fail to reliably project ISMR due to high inter‐model uncertainty and internal variability. The significant but inconsistent future changes in ISMR projected for a major portion of the Indian subcontinent, in contrast to earlier studies, pose extreme challenges to climate change impact assessments and adaptation/mitigation planning.

Climate change impact assessment of a river basin using CMIP5 climate models and the VIC hydrological model - Narendra Hengade; T. I. Eldho; Subimal Ghosh (Hydrological Sciences Journal, 2018)

Impact of active and break wind spells on the demand–supply balance in wind energy in India - Sumeet Kulkarni; M. C. Deo; Subimal Ghosh (Meteorology and Atmospheric Physics, 2018)

Decadal temperature predictions over the continental United States: Analysis and Enhancement - Kaustubh Salvi; Gabriele Villarini; Gabriel A. Vecchi; Subimal Ghosh (Climate Dynamics, 2017)

Spatio-temporal analysis of sub-hourly rainfall over Mumbai, India: Is statistical forecasting futile? - Jitendra Singh; Sheeba Sekharan; Subhankar Karmakar; Subimal Ghosh; P E Zope; T I Eldho (Journal of Earth System Science, 2017)

Do dynamic regional models add value to the global model projections of Indian monsoon? - Swati Singh; Subimal Ghosh; A. S. Sahana; H. Vittal; Subhankar Karmakar (Climate Dynamics, 2017)

Projections of Extreme Dry and Wet Spells in the 21st Century India Using Stationary and Non-stationary Standardized Precipitation Indices - Kaustubh Salvi; Subimal Ghosh (Climatic Change, 2016)

Credibility of statistical downscaling under nonstationary climate - Kaustubh Salvi; Subimal Ghosh; Auroop R. Ganguly (Climate Dynamics, 2016)

Intensification of future severe heat waves in India and their effect on heat stress and mortality - Kamal Kumar Murari; Subimal Ghosh; Anand Patwardhan; Edoardo Daly; Kaustubh Salvi (Regional Environmental Change, 2015)

Prediction of extreme rainfall event using weather pattern recognition and support vector machine classifier - Munir Ahmad Nayak; Subimal Ghosh (Theoretical and Applied Climatology, 2013)

Modeling Uncertainty Resulting from Multiple Downscaling Methods in Assessing Hydrological Impacts of Climate Change - Subimal Ghosh; Sudhir Katkar (Water Resources Management, 2012)

Prediction of daily rainfall state in a river basin using statistical downscaling from GCM output - S. Kannan; Subimal Ghosh (Stochastic Environmental Research and Risk Assessment, 2011)