The current study investigates the climatic extremes using high-resolution datasets over densely populated cities of the Middle Gangetic Plain, India. Extreme weather events significantly impact urban infrastructure, public health, and the economy, with these effects intensifying due to climate change. The Climpact2 package was used to analyse nine temperature-based and six precipitation-based indices from high-resolution Climate Hazards Center datasets (CHIRTS and CHIRPS) spanning from 1983 to 2016. The Mann–Kendall test and Sen’s slope estimator were employed to evaluate trends in extreme climatic events. Results reveal a significant increase in warm temperature indices and a decrease in cold temperature indices across most cities. Warm days (TX90P) increased up to 0.406

Urban regions of the Gangetic Plain are experiencing increasing exposure to climatic extremes, making them critical hotspots for assessing the impacts of regional climate variability and rapid development. This study investigates the spatiotemporal patterns of temperature and precipitation extremes across 16 smart cities located in Delhi, Uttar Pradesh, Bihar, and West Bengal. High-resolution precipitation data from Climate Hazards Center InfraRed Precipitation with Station data (CHIRPS; 1981–2022) and temperature data from Climate Hazards Center InfraRed Temperature with Station data (CHIRTS; 1983–2016) were used. A suite of standardized indices from the Expert Team on Sector-specific Climate Indices (ETSCI) and the Expert Team on Climate Change Detection and Indices (ETCCDI) was computed, and trends were analyzed using the Mann–Kendall test and Sen’s slope estimator, with statistical significance assessed at p-value < 0.05. The results reveal a pronounced and statistically significant warming trend across most cities, characterized by a significant increase in warm extremes (TX90p and TN90p; p-value < 0.05 to p-value < 0.001) and a concurrent significant decline in cold extremes (TN10p; p-value < 0.001). Several cities, including Patna, Biharsharif, and Lucknow, exhibit strong intensification of heat stress, along with increasing warm spell duration (WSDI), indicating a shortening and weakening of winter conditions. Extreme precipitation indices exhibit high spatial variability and are largely statistically non-significant, though notable localized changes are evident. Cities such as Agra and New Delhi show declining trends in extreme rainfall indices, including RX1day and R95p, whereas cities such as Kanpur and Jhansi demonstrate increasing trends in heavy rainfall frequency (R10mm) and total precipitation (PRCPTOT, significant in Kanpur). Cities including Patna and Bhagalpur indicate modest increases in heavy precipitation indices (R95p and PRCPTOT) and wet spell duration (CWD). Additionally, some locations show increasing consecutive dry days (CDD), suggesting enhanced rainfall intermittency. Overall, the findings indicate a transition toward intensified heat extremes and spatially heterogeneous precipitation behaviour, with emerging risks of both extreme rainfall and dry spells. These changes have significant implications for urban climate resilience, emphasizing the need for adaptive planning strategies to mitigate heat stress, manage urban flooding, and address water resource variability in rapidly growing cities of the Gangetic Plain.

Rainfall and temperature variability serve as crucial indicators of hydroclimatic hazards, including floods, droughts, heatwaves, and cold spells. While such events may arise from a single variable reaching extreme levels, they often result from the interplay of multiple climatic factors. This study examines the spatio-temporal variability of compound extreme events (CEEs) over the semi-arid Ken and Betwa River catchments in Central India. Although these regions primarily receive rainfall during the Indian Summer Monsoon (ISM) season, they have experienced a post-2000 drying trend along with rising temperatures. A significant negative correlation between rainfall and temperature indicates rainfall suppression under hotter conditions due to enhanced atmospheric stability and reduced moisture availability. The analysis further shows that extreme wet and dry events have declined in the Betwa basin, while the Ken basin exhibits an increase in extreme dry events and a decrease in wet extremes. Cold extremes (T10) have also shown a decreasing trend across both regions. Investigation of different combinations of rainfall and temperature extremes reveals that moderate and extreme warm-dry CEEs have intensified over the past four decades, emerging as the most dominant compound events. The persistence of these events is largely driven by wind patterns and convective inhibition energy (CIN) in the case of moderate events, and by moisture transport and divergence for extreme ones. The intensification of such CEEs poses substantial risks to regional agriculture, eco-hydrological systems, and socioeconomic stability. Composite Resilience Index (CRI) was developed at the district level, integrating indicators like the Human Development Index, Multidimensional Poverty Index, and literacy rates. Results reveal that Ashoknagar, Shivpuri, Lalitpur, and Chhatarpur are the relatively low-resilience districts, while Bhopal, Sagar, Jhansi, and Hamirpur exhibit higher resilience. Overall, the findings underscore the urgent need for climate-informed policies and adaptive strategies to ensure water sustainability and socio-economic stability in the Ken–Betwa river catchments under a warming climate.

This study investigates past and future trends of extreme precipitation indices: Consecutive Wet Days (CWD), Consecutive Dry Days (CDD), and Total Precipitation across four major smart cities of the Gangetic Plain: Delhi, Lucknow, Patna, and Kolkata. Climate Hazards Infrared Precipitation with Stations (CHIRPS) data has been used as the past observational data for calibration and validation of the model. Future projections were analysed using statistically downscaled outputs from three CMIP6 global climate models (CanESM5, MPIESM1-2, and NorESM2) under two Shared Socioeconomic Pathways (SSP2-4.5 and SSP5-8.5). The non-parametric Sen’s slope estimator was applied to quantify trend magnitudes, while statistical significance was assessed using the Mann–Kendall test. Past records revealed mixed patterns in CWD and CDD, with a general increase in Total Precipitation in Delhi, Lucknow, and Patna, and a decline in Kolkata. Future projections indicate marked scenario-dependent variations, with SSP5-8.5 consistently showing stronger and statistically significant changes compared to SSP2-4.5. Under SSP5-8.5, CWD increases are most prominent in Delhi (0.039 days/year in CanESM5, 0.117 days/year in NorESM2) and Kolkata (0.241 days/year in CanESM5), suggesting longer wet spells. CDD is projected to decline in most cities, particularly in Lucknow (-0.596 days/year) and Patna (-0.166 days/year) under CanESM5 and MPIESM1-2, respectively, indicating shorter dry periods. Total precipitation shows a substantial increase, with the highest trends projected over Delhi (10.19 mm/year), Lucknow (8.77 mm/year), and Kolkata (8.18 mm/year) in the CanESM5 model. The findings point toward a future shift toward wetter conditions, with prolonged wet spells and higher annual rainfall totals, increasing the potential for urban flooding and waterlogging. These results underscore the need for targeted climate adaptation strategies, improved stormwater management, and enhanced resilience planning to address the increasing risks of extreme precipitation events in rapidly growing urban areas of the Gangetic Plain.

This research aimed to elucidate the components of rainfall variation, their influence on the natural vegetation growing season and consequent impacts on the livestock population. This study evaluates the influence of rainfall metrics and drought on vegetation and livestock in Central District, Botswana. It uses Pearson correlation analysis to assess the relationships between rainfall metrics, drought, vegetation and livestock. Trends were analysed using Mann–Kendal and Sen's slope analysis. It was found that rainfall variability and drought frequently occur in Central District, with continuing effects on vegetation and livestock. From 1990 to 2020, the district experienced moderate droughts on cycles of approximately alternating years. Severe drought occurred in 2003, and 2000 was a wet year. No significant trend was observed in rainfall metrics. The normalised difference vegetation index (NDVI), and the number of cattle and goats significantly declined. Annual NDVI shows a significant relationship with the number of rainy days, drought and consecutive wet days; cattle numbers are negatively correlated with consecutive dry days. Seasonal results show that NDVI is highly correlated to the number of rainy days in April–June (AMJ) and October–December, and NDVI is correlated to the standardised precipitation evapotranspiration index (SPEI) during AMJ and July–September. The study findings revealed a seasonal and annual relationship between rainfall metrics, SPEI 12, livestock (goats, sheep and cattle population) and NDVI in the Central District of Botswana.

This study downscaled the future precipitation extreme indices over the smart cities of the Gangetic Plain using the Statistical Downscaling Model (SDSM). The analysis focused on three Global Climate Models (GCMs): CanESM5, NorESM2-MM, and MPI-ESM1-2 h, under two Shared Socioeconomic Pathways (SSP245 and SSP585). Historical precipitation data from the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) (1981–2014) and National Center NCEP gridded data were used to calibrate and validate the model. The study examined extreme precipitation indices such as RX1day (Maximum 1-day precipitation), RX5day (Maximum consecutive 5-day Precipitation), R20mm (Annual count of days when PRCP > = 20 mm), and R95P(Annual total PRCP when RR > 95th percentile), which quantify the intensity, frequency, and contribution of extreme rainfall events. The results reveal distinct trends in extreme precipitation across the cities under both SSP245 and SSP585. Under SSP245, moderate increases in extreme precipitation indices were observed, particularly in CanESM5, with significant trends emerging under SSP585. Capital cities Delhi, Lucknow, Patna, and Kolkata showed strong increases in extreme precipitation indices under SSP585, highlighting the potential exacerbation of urban flooding risks and infrastructure strain in the future. The CanESM5 model generally showed higher sensitivity and trends, indicating a higher frequency and intensity of extreme rainfall events under SSP585 scenarios. The study provides valuable projections of future climate extremes, offering insights into potential climate risks for urban infrastructure, water management, and disaster preparedness in the smart cities of the Gangetic Plain.

The rapid population growth in the last few decades has resulted in urbanisation at a very fast pace. The world is facing various environmental problems and health issues that are impacted by urbanisation. It multiplies due to the increase in land surface temperature and aerosol loadings in urban areas which forms Surface Urban Heat Islands (SUHI) and Urban Pollution Islands (UPI). Of late it is noticed that the elevated aerosol loadings from the agricultural/crop residue burning or stubble burning (CRB/ARB/SB) is causing further degradation to the urban atmospheric environment in some parts of the country. However, the middle Gangetic plain (MGP) or the present study area is yet to be evaluated specifically in the context of SUHI and Urban AOD variation in ARB season. The cities of the Middle Gangetic Plain (Varanasi, Patna, and Gaya, ) have been focused in the present study, during the post-monsoon season of India, which corresponds with the harvesting period of rice crop. The present study is carried out to measure variations in SUHI intensity (SUHII) and Urban aerosol loading, with special reference to the ARB at the same time. This study involves the use of MODIS data products viz. MOD11A1 and MCD19A2 along with several other datasets from GWIS and other sources for estimating the variations in LST, variation in the aerosol loadings and land cover-based fire and emission data in the past two decades. It has been found that SUHII, urban AOD, regional particulate matter load and burned area are increasing, indicating noticeable deterioration of the atmospheric environment. The Sen’s slope estimator indicated an increase of 0.01℃ to 0.03℃ per year in monthly average SUHII across all the studied cities. Whereas AOD is found to be increased by 0.008–0.02 per year. Except for SUHII over Patna in October all cities register a positive increasing trend in MK test for urban AOD and SUHII. In the light of this increasing trend future mitigation policies are strongly needed.

In this study, we used aerosol, cloud properties and precipitation data from the Coupled Model Intercomparison Project Phase 6 (CMIP6) model to study the association of aerosol with cloud and precipitation over the Gangetic plain of India. Additionally, changes in aerosol, cloud properties and precipitation in the future have also been studied here. The results show good agreement in spatial distribution of Aerosol Optical Depth (AOD), Cloud Water Path (CWP), Cloud Effective Droplet Radius (CEDR) and precipitation of CMIP6 model simulation under historical experiment and observed product. It was found that AOD and CWP indicate significant increasing trend during 1850–2014. The AOD and CWP reveal an increasing trend of 0.1/century and 24 g/sq.m century, respectively during 1850–2014. To understand the relationship between aerosol, cloud and precipitation, Pearson’s correlation is computed and it is found that AOD and CEDR is showing significant negative correlation. Moreover, AOD is positively correlated with CWP and precipitation. As per future prediction study for the period 2015–2100, ADO and CEDR respectively show significant decreasing and increasing trend.

The most important parts of any flight are landing and takeoff, and an aircraft's takeoff configuration must balance the regulated takeoff weight, runway length, and weather conditions to ensure a safe departure and arrival. In addition to runway length, wind, temperature, pressure, and visibility determine the total allowed takeoff weight and the economic viability of any trip. Thus, any meteorological office involved in flight planning and operation at any airport must accurately assess these factors, known as takeoff data. This research paper suggests multivariate self-supervised LSTM-based models to accurately predict the temperature and pressure (MSLP) of the takeoff data. The suggested prediction algorithms are based on deep neural networks (LSTM), making them straightforward to design and resource-efficient. Based on this approach, a Nowcast of temperature and pressure for the next one to six hours could be generated using the time series multivariate dataset for airport temperature and pressure (representative station: Patna Airport) with input features including date and time, temperature, atmospheric pressure, humidity, dew point temperature, wind direction, wind speed, cloud amount, and present and past weather. Pearson correlation coefficients determined input features. The model's efficacy and accuracy are measured by comparing anticipated temperatures and pressure (MSLP) to observed temperatures and using several performance indicators. This technique aims to construct and implement robust and dynamic self-supervised LSTM models to automate takeoff data, which is essential for flight safety and efficiency.

This current research work is focused on the performance of the Coupled Model Intercomparison Project (CMIP6) models in replicating Indian Summer Monsoon (ISM) rainfall patterns, mainly focusing on the Gangatic Plain, spanning from 1979 to 2014. The evaluation employs rigorous validation against rainfall data from the Indian Meteorological Department (IMD) and includes models with a resolution of 100km or less. Results highlight three standout models CMCC-CM2-SR5, EC-Earth3-AerChem, and CMCC-ESM2—for their commendable accuracy in capturing rainfall distribution in the Gangatic Plain. Monthly assessments reveal that these models closely mimic observed rainfall, unlike other models such as CMCC-CM2-HR4, CNRM-CM6-1-HR, EC-Earth3-CC, and MPI-ESM1-2-HR, which fail to replicate observed rainfall patterns. The simulated seasonal cycle of rainfall in CMCC-CM2-SR5 aligns well with observed patterns, especially for July, August, and September. Although there are some amplitude differences, CMCC-CM2-SR5 accurately represents the range and median of observed rainfall during these months. Interannual fluctuations in mean rainfall during the JJAS period can also be derived from observed data. Furthermore, CMCC-CM2-SR5, EC-Earth3-AerChem, and CMCC-ESM2 successfully capture interannual variability in July, August, and September, indicating substantial rainfall variability on a sub-seasonal scale across the Indian subcontinent. However, the assessment emphasizes the need for improved accuracy in depicting rainfall variability in CMIP6 models to bridge existing gaps between simulated and observed rainfall. Nevertheless, the study emphasizes the ongoing necessity for enhancements to ensure more accurate simulations and a closer match to actual rainfall variability in the examined CMIP6 models.

The current study investigates the relationship among Aerosol Optical Depth (AOD), rain rate in aerosol types (fine-mode and coarse-mode), and meteorological conditions (such as relative humidity and vertical velocity) during Indian Summer Monsoon (ISM) over regions of the Gangetic plain (GP) and the Modified Core Monsoon Region (MCMR) of India. The Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products, including AOD and Angstrom Exponent (AE), the observed rainfall gridded data of India Meteorological Department (IMD) and reanalyzed data of relative humidity (RH), vertical velocity (VV) and wind speed and direction as meteorological variables from European Center for Medium Range Weather Forecasting (ECMWF) Reanalysis v5 (ERA5) are used for the period of 2003–2021. It is noticed that regions of GP and MCMR experienced significant rainfall suppression with increasing aerosol loading in moderately polluted condition and beyond moderately polluted condition, the different rain rate responses to aerosol loading is observed. Different aerosol types and meteorological conditions may have different influences on the AOD-rain rate relationship. It is found that an increase in AOD may suppress or enhance the rain rate in a region like GP where high RH condition prevails, as well as the presence of fine-mode aerosol can enhance the rain rate in a highly polluted condition (AOD ≥ 0.4). An increase in AOD may enhance the rain rate in conditions of high and low RH over the regions. In high and low updraft conditions, an increase in AOD suppresses the rain rate. Additionally, high (low) RH and high (low) updraft promote high (low) rain rate over the regions.

In recent years, the Gangetic Plain of India has witnessed a noticeable decline in the number of rainy days48. This study explores this phenomenon by analysing meteorological data from the Indian Meteorological Department (IMD), specifically utilizing the IMD's grid with a resolution of 0.25 degrees by 0.25 degrees dataset. Through rigorous data analysis and statistical methods, we reveal a significant and quantifiable reduction in how frequently rainy days in49 this agriculturally crucial region. We calculated the rainy days decadal-wise that is 1901-10 to 2011-20 and found the decrease in rainy days. This declination in rainy days is more in the month of August (sen’s slope = -0.125) while June and September have less while July has no such declination type trends(sen’s slope = 0.0). This decline in rainy days has raised concerns about its potential impact on agriculture, water resources, and the livelihoods of the millions of people dependent on the Gangetic Plain's agrarian sector. Also, we found that the eastern part of Gangetic Plain receives more amount of rain while the western part less. So, this study emphasizes the need to comprehend how climate change is affecting rainfall patterns and the necessity of taking proactive steps to solve the problems brought about by fewer rainy days in the Gangetic Plain48.

The climate change assessment in the context of urban areas is very crucial for policy making regarding hazard mitigation and citizen’s health, especially for the smart cities. The past climate assessment and present climate monitoring is somewhere easy, but the projection of future climate at city level is very difficult as most climate models fail to resolve the cities spatially. Over the highly populated areas like the region of Gangetic Plains the precise city specific climate projection becomes more important. The present study aims to project the future temperature, covering both minimum temperature (Tmin) and maximum temperature (Tmax) and analyse the extreme indices over smart cities in the Gangetic Plains, as one of the pioneer works using CMIP6 model’s downscaled outputs using SDSM model. The study reveals that these smart cities are likely to experience warmer and more extreme temperatures in the upcoming decades. The future temperature projections were generated under two emission scenarios (SSP245 and SSP585), and for near future (2030–2065) and far future (2066–2100) periods. The drastic change in minimum temperature (Tmin) was observed over New Delhi, Prayagraj, Kolkata, and Lucknow by the end of the century under SSP585. Four extreme temperature indices were also analyzed for future time series: (1) TXgt40(No. of days Tmax > 40ºC); (2) TNlt10(No. of days when Tmin < 10ºC); (3) TX90p (Percentage of days when Tmax > 90th percentile); and (4) TN10P (Percentage of days when Tmin < 10th percentile). The increasing trend of warm temperature Indices and decreasing trend of cool temperature indices were observed over all the stations. The drastic change in extreme temperature indices may have a significant effect on urban climate, it could impact public health by increasing the incidence of heat-related illnesses such as heat stress or heat exhaustion. The present study can be also utilized as a probable baseline for assessing the extreme climate conditions in the future. As this study is one of the very first attempts under the aspect of smart cities and thus it may help in developing early warning systems for smart cities in the Gangetic Plain.

Landing and takeoff are the most crucial phases of any flight; in particular, the takeoff configuration of an aircraft is a delicate balance between the regulated takeoff weight, runway length available, and prevailing meteorological conditions to execute a safe takeoff. Apart from the fixed parameters like runway length, four variable meteorological parameters, i.e., wind, temperature, pressure, and visibility, govern the total permissible takeoff weight and thus influence the economic or commercial viability of any flight. Therefore, an accurate assessment of these parameters, which are collectively called takeoff data, is an important responsibility of any meteorological office associated with flight planning and operation at any airport. It is suggested in this research paper that instead of forecasters making decisions based on numerical weather prediction (NWP) models, multivariate self-supervised LSTM-based models should be used to make accurate predictions of temperature and pressure (mean sea level pressure or MSLP) parameters of takeoff data. As the proposed prediction algorithms are based on deep neural networks (LSTM), they are relatively easy to develop and require less time and resources. Based on this approach, a Nowcast of temperature and pressure for the next one to six hours could be generated using the time series multivariate dataset for the temperature and pressure of the airports (representative station: Patna Airport) with input features including date and time, temperature, atmospheric pressure, humidity, dew point temperature, wind direction, wind speed, cloud amount, and present and past weather (like fog, dense fog, drizzle, etc.). The input features were selected based on the best Pearson correlation coefficients. The predicted temperatures and pressure (MSLP) are compared with the observed temperatures, and the models' efficacy and accuracy are assessed based on the different performance measures. The ultimate objective of this approach is to develop and deploy robust and dynamic self-supervised LSTM models to automate the crucial takeoff data that is so crucial for the safe and economic operation of any flight.

The Tropical Cyclonic Disturbances (TCDs) over the Bay of Bengal (BoB) have always disrupted life, economy and environment across the coastal regions. The present study evaluates the ability of COoordinated Regional Climate Downscaling Experiment (CORDEX) constituting regional climate model (here REMO2009) in simulating the behaviour of some precursors of TCDs, frequency of TCDs and their intensity over the BoB. Furthermore, the impacts of El Niño Southern Oscillations (ENSO) and Indian Ocean Dipole (IOD) are addressed to determine the sensitivity of the model in capturing large-scale ocean-atmosphere coupled phenomena. The model outputs (resolution 0.44° × 0.44°) are evaluated against the recorded observations of India Meteorological Department and ERA-Interim reanalysis (resolution 0.25° × 0.25°) over the time period 1979–2005. Evaluation of TCD frequencies and intensities on a year-to-year basis shows the model performing reasonably well against observations but intensity is largely reduced. Also, we find an overestimated number of TCDs in the model as pre-monsoon (post-monsoon) shows + 195% (+ 80%) more TCDs. The large-scale environmental fields associated with TCDs show spatiotemporal biases of varying magnitudes in the model however are consistent in capturing TCDs and their behaviours. The mean climatology shows clear differences in environmental fields during days with TCDs and without TCDs. The genesis geolocations in observations are coherent with their environmental fields and are firmly reproduced in the model albeit with spatial differences. The intensity in the model is found to be mostly low showing weak TCDs besides overestimating (underestimating) TCDs of moderate (high) intensities. The REMO2009 model is found satisfactorily simulating the TCDs (year-to-year basis) and associated large-scale environmental fields against the observations and reanalysis. The impacts of large climatic teleconnections (ENSO and IOD) are also captured in the model with warm ENSO phase suppressing the TCDs activities while cold phase triggering the TCDs. On the similar watch, a negative dipole over the Indian Ocean triggers the TCDs while a positive dipole suppresses the TCDs formation in the model. The present study using the regional climate model (RCM) REMO2009 is moreover a needed baseline for the future projections and evaluation of other RCMs under the CORDEX domains.

The urbanisation and its detrimental impact on climate is a well-documented phenomenon in today’s world, but research documenting the Urban Pollution Island (UPI) especially over South Asia is seldom found. With the advancement of the satellite datasets, the quantification of UPI has become possible only in recent years. When measured using satellite data, the UPI is the spatial anomaly of Aerosol Optical Depth (AOD) over an urban area with reference to a nearby non-urban zone. UPI may influence energy budget, precipitation patterns and human health over the city. In the present research, it has been attempted to analyse the climatology and characteristics of UPI and its association with the Surface Urban Heat Island (SUHI) over six cities (Patna, Gaya, Ranchi, Jamshedpur, Bardhaman and Siliguri) from eastern India, which is a highly populated region and infamous for climatic concerns. Alongside, a Surface PM2.5 data is also investigated further, to find heat and pollution island links. The UPI–SUHI interactions have been evaluated and found to be very distinct for each city. It is found that high urban AOD value can be noticed irrespective of the UPI magnitude over Patna. Bardhaman has exhibited very high AOD (> 3.0) even in very low UPI conditions. Jamshedpur’s urban loadings found to be contributing somewhere to UPI formations. UPII has also shown a clear sign of a seasonal cycle across the cities. In Patna, increase in PM2.5 may be linked to SUHII in medium loading cases and very high PM2.5 loadings (> 200 μg/m 3 ) result in low average SUHII. It may be summarised that Patna, Gaya and Bardhaman are exhibiting high surface PM2.5 loads over urban zones, whilst Ranchi, Siliguri and Jamshedpur have much cleaner urban air. The Mann–Kendall test and Pettitt’s test also detected significant increasing trend and change point in recent times for UPI intensity. The well-developed UPI system shows an exigency of more in-depth studies to mitigate the detrimental effects of UPI–SUHI in upcoming times.

The detection of weather and climate change caused by urbanisation is an important issue to understand and future projection of local weather change due to anthropogenic activities. Observational and climatological studies of alteration in convective phenomena over and around the urban area are reviewed with a focus on urban-modified downwind enhancement of rainfall. Causative factors for the alteration of urban precipitation can be urban heat island, surface roughness and anthropogenic aerosol. Monitoring of urban climate through high-resolution datasets was found to be quite important in today's era of climate change. A detailed study through high-resolution CHIRPS-gridded data has been done for the cities of Patna and Gaya. The Mann-Kendall test and Pettitt's test also indicated a changing trend in the rainfall intensity regime in the recent past for Patna, while a decreasing rainfall over Gaya has been envisaged by time-series analysis.

Tropical Cyclonic Disturbances (TCDs) are one of the most extreme meteorological calamities bringing destruction to life and livelihood in the coastal societies across the globe. With the rising concerns of climate change today, addressing the TCDs in future scenarios under the representative concentration pathways (RCPs) in climate models becomes a necessity. The current study investigates the frequency and intensity of these cyclonic systems in future climate over the Bay of Bengal (BoB) which is one of most vulnerable regions on earth for deadliest TCDs. To assess the TCDs frequency and intensity, we have considered TCDs in regional climate model REMO2009 and RegCM4 in future climatic conditions. The future climatic conditions include the intermediate emissions (IE) represented as RCP4.5 (R4.5) and high emission (HE) pathways i.e., RCP8.5 (R8.5). For this, we have considered the upcoming decades 2031-2060 (as near future climate) at model horizontal resolution 0.44°x0.44° (spatial resolution ~ 50 km) under both RCPs in both models i.e., REMO2009 under R4.5, RegCM4 under R4.5, REMO2009 under R8.5 and RegCM4 under R8.5. The projected TCD frequencies in the models under both the RCPs show high occurrence frequencies. Further, we observe a bimodal characteristic in the occurrence with October as primary TCD active month and May as secondary in almost all conditions. However, highly intense TCDs are more dominant in the month of May. The projected TCDs in future emissions scenarios likely show slightly increased TCDs besides surge in the intensity. The current results possibly suggest more potential destructive impacts due to TCDs on the coastal societies lying beside the BoB in upcoming decades. Thus, the present study is likely to help in framing TCDs associated mitigation and adaptation policies by the apex decision making authorities.