The regional impacts of extreme climate events have been worsening over the last two decades and have intensified over the years. It has a major effect on health, agriculture and the hydrological cycle on a large scale therefore, it’s very important to determine and quantify the future duration and intensity of such events. This study explores the Spatio-temporal variation in seven selected extreme temperature indices for the period 2025-2099 using five Coupled Model Intercomparison Project Phase 5 (CMIP5) model ensembles under Representative Concentration Pathways (RCP) 4.5 and 8.5 emission scenarios over Gujarat, India. The results demonstrated that minimum and maximum temperatures are projected to rise significantly in both scenarios, with the most significant increase expected during the post-monsoon season. We could see a rise of more than 4.6°C in maximum temperature under higher scenario by the end of the 21st century. The warming and cooling indices show a significant increase and decrease, respectively, irrespective of the regional diversity. Overall, most indices show a substantial change except for the diurnal temperature range however, some models (BNU-ESM and MPI-ESM-LR) seem to have greater variability when compared to the multi-model mean (MMM).
Keywords. CMIP5; Temperature extremes; ETCCDI; RCP; Trends
1. Introduction
The global and regional mean surface temperature has increased by 0.85°C in recent decades (IPCC 2013; Lun et al. 2021). A rise in temperature by 1°C increases the moisture in dry air by 7%, leading to intensification of hydrological cycles and disrupting climate balance (Jibhakate et al. 2023). The warming of climate has been unequivocal since the 1950s due to anthropogenic activities, with a rise in greenhouse gases (GHGs). In the Paris Agreement, it was suggested to limit the temperature increase to 1.5°C is still a big task to achieve. Meanwhile, the intensity of warm nights and hot days has significantly increased at the global and regional scale in recent decades (Alexander et al., 2006; Dhorde et al., 2016; Agnihotri et al., 2018; Khan et al., 2020; Kumar and Pathak, 2025).
Due to the increased GHGs, the projected temperature analysed using CMIP5 models is likely to increase significantly around the globe. The average temperature is expected to rise by 1.5°C to 7°C in different parts of the world by the end of the 21st century under various emission scenarios (Wang and Chen, 2014; Tian et al., 2015; Ozturk et al., 2017; Chevuturi et al., 2018; Almazroui et al., 2020). This temperature may rise by 0.63°C per decade and result in extreme events such as prolonged warm spells, decreased diurnal temperature range, winter warming and seasonal shifts etc. (Chong-Hai and Ying 2012; Thibeault and Seth 2014; Lindvall and Svensson 2015; Singh et al. 2015; Kalita et al. 2023). However, in South-East Asia, a multi-decadal variability in extreme events has been observed. Largely due to the effect of increased sea-level pressure gradients over tropical regions which have significantly influenced daily minimum temperature range (Chevuturi et al. 2018; Ozturk et al. 2021; Ren et al. 2021). The mechanism of temperature variability particularly in South Asia, is even more complicated as diverse and heterogeneous forcings are characterised by growing population, urbanization and energy demand which play a vital role. Therefore, regional assessments of the temperature uncertainties are crucial to understanding the relative influence of such forcings based on climate model simulations (Hu et al. 2014; Tian et al. 2015; Singh et al. 2019; Oktaviana et al. 2026; Samaei and Riffat 2026).
India is one of the most diverse countries, with varied climate zones and topography. The spatial and temporal variability of change in temperature with different magnitudes reflects the complex setting of the Indian climate (Kothyari and Singh 1996; Pal and Al-Tabbaa 2010; Panda et al. 2014; Dimri 2019; Kalyan et al. 2021). As a result, different temperature changes have been reported from all parts of the country. Over 80% of the area experienced a maximum temperature of >40°C of different intensities, and the minimum temperature has risen by more than 0.02°C per year in recent decades (Dhorde et al., 2016; Chandole et al., 2019; Vieira et al., 2023). The diurnal temperature range (DTR) change was not significant except in the Northern and Southern parts, where a decreasing trend was observed with an increase in maximum temperature by 2.3°C (Roy and Balling 2005; Jain et al. 2013; Nengzouzam et al. 2019; Roy 2019; Shafiq et al. 2019). However, the change in extreme events such as hot days and hot nights shows a significant increasing trend while cold days and nights show a significant decreasing trend across the country, including the East Coast, West Coast and interior peninsula etc. (Kothawale et al., 2010; Vincent et al., 2011; Revadekar et al., 2012; Chakraborty et al., 2016).
Similarly, future projections are showing great variability in temperature changes around the Indian subcontinent. Basha et al., 2017 reported that the temperature will rise till 2050; afterwards, it may decrease, a pattern also noted by Krishnan et al., 2020; Ravindra et al., 2024. However, they warned that if the current pace of temperature change continues, we may see a rise of ~5°C by the end of the 21st century which was also supported by Paul and Maity, 2023. Overall, the mean temperature in India is expected to rise by 3.3-4.8°C by the 2080s (Sahany et al. 2019) while eastern and western regions are projected to experience a rise of 3.08-3.44°C and 2.45-4.27°C respectively (Sharma et al. 2018; Vijayakumar et al. 2021). The frequency of hot and cold extremes over the country is likely to rise significantly. For example, the Warm Spell Duration Index will increase by 131 days, whereas cold extreme values could vary between 0.58-2.29°C, particularly in the northwest and northeast regions (Panday et al. 2015; Pangaluru et al. 2018; Yaduvanshi et al. 2021).
However, the highest ambiguity in historical and projected climate trends has been observed over the state of Gujarat (Lunagaria et al. 2012), which is also the focus area of this study. In Gujarat, both minimum and maximum temperatures have been increasing during the winter season with a trend of 0.012°C/year (Ray et al., 2009; Nath et al., 2023). The analysis from the LARS-WG model indicates that maximum and minimum temperatures could rise by 3.3°C and 4.5°C respectively with a corresponding intensification in extreme events by the end of the 21st century (Sarkar and Chicholikar 2017). The rising number of heat events will have a significant impact on the frequency of drought occurrence in parts of Gujarat, a region already highly prone to drought. Simultaneously, it will also lead to agricultural vulnerability and could reduce productivity by 36% (Dash and Mamgain 2011; Patel et al. 2013; Jayaraman and Murari 2014; Bandyopadhyay et al. 2016; Saxena and Mathur 2019). The warming of the Indian subcontinent (especially the Gujarat region) is greater and more irregular as compared to the global average under all historical and projected GCMs.
To understand future temperature extremes, Global Circulation Models (GCMs) provide a robust framework for representing RCP scenarios taking carbon cycle, cloud and physical process feedback mechanisms into account at a regional scale (Taylor et al. 2012; Kamruzzaman et al. 2021). The CMIP5 have simulated the temperature projections across models and demonstrated an ability to better represent temperature than precipitation data at spatial scales (Aloysius et al. 2016; Xiong et al. 2021; Chen et al. 2024). However, due to the uncertainty (under/over-estimation) in models, some researchers also suggested using the bias-corrected multi-models ensemble means which depicts a good representation of long-term trends, especially over South Asia (Kumar et al. 2013; Raghavan et al. 2018; Peng et al. 2019; Shiru et al. 2020; Deng et al. 2021; Supharatid et al. 2022).
Since India has great climatic variability at a regional scale, the current study will illustrate and help to understand the projected duration and intensity of the temperature extremes over Gujarat. Furthermore, it is suggested by multiple researchers to use CMIP data and RCP scenarios to have high-resolution and regional-specific impact assessments of temperature extremes, with an emphasis on environmental aspects (Pal and Eltahir, 2016; Almazroui et al., 2020; Devi et al., 2023; Maurya et al., 2023; Wu et al., 2026). Though, over Indian sub-continent CMIP5 projections have been used by some researchers but large geographical area often leads to cognitive uncertainty and inconsistency. The special emphasis and the dedicated regional projections will reduce these inconsistencies. Therefore, the projected assessment over the Gujarat region will be crucial for addressing temperature extremes and reducing model inconsistency based on anthropogenic global warming and emission scenarios.
2. Study area
Gujarat is a state situated between 20°06’N to 24°42’N latitude and 68°10’E to 74°28’E longitude on the western coast of India. It comprises 33 districts with an area and population of 196,024 km2 and 60.4 million respectively (Census 2011). It is the 5th largest state as per area, bordered by Rajasthan, Madhya Pradesh, Maharashtra, Arabian Sea and the Sindh province of Pakistan (Nath et al. 2023). Gujarat falls in the northwest (NW) and west coast (WC) of India as categorized by the Indian Institute of Tropical Meteorology, Pune (www.tropmet.res.in) based on homogeneous temperature regions (Dash and Mamgain 2011; Kalyan and Pathak 2024). Due to its proximity to the Thar Desert to the north, it has a mix of sub-humid, arid and semi-arid climate conditions spread over various regions of the state. The state’s climate is further characterized by distinct topographic regions namely north, central and south Gujarat, Saurashtra, Eastern tribal belt and Kachchh in the northeast (Bandyopadhyay et al. 2016). Temperature varies from a maximum of 45°C to a minimum of 6°C in summer and winter while it gets annual rainfall ranges between 250mm to 1500mm (Ray et al., 2009). The state gets 90% of the total rainfall only in the monsoon season (June-September). Due to the uncertainty in the climate and spatial variation in rainfall and temperature, Gujarat is prone to both floods and drought conditions, affecting large-scale monsoon circulation and millions of people through its teleconnection to the regional and global circulations (Sarkar and Chicholikar 2017; Chevuturi et al. 2018).
3. Data and Methodology
3.1. Model dataset
For the current study, bias-corrected daily maximum and minimum temperature data for RCP4.5 and RCP8.5 scenarios from five CMIP5 model ensembles (Table 1) for the period 2025-2099 were used. The projected data, derived from various General Circulation Models (GCMs) and reanalysis simulations have been used which have been suggested globally for climate change studies (Dash et al. 2012; Kharin et al. 2013; Koutroulis et al. 2016; Ntoumos et al. 2020; Kamruzzaman et al. 2021; Zhou et al. 2023). For historical comparison, India Meteorological Department (IMD) gridded data (1.0°×1.0°) have been used for 1981-2004 (see Figure S9, S10 and Table S5 of the supplementary file). The CMIP5 models were selected based on their performance in simulating temperature accurately (Mishra et al. 2018; Kamworapan and Surussavadee 2019). It provides GCM simulations of multiple representative concentration pathway scenarios allowing precise assessments of future climate conditions at the regional scale (Seo et al., 2014; Song et al., 2021). Models such as BNU-ESM, CNRM-CM5, MPI-ESM-LR and MPI-ESM-MR are better at simulating Spatio-temporal temperature variability over India (Das et al. 2018; Ullah et al. 2020). Additionally, these model data are downscaled and provided by the Urban Climate Downscaling Portal over India and are selected based on their simulation capacity and correlation with a particular predictor such as air temperature, wind velocity and humidity etc. at various pressure levels. Therefore, a re-gridded dataset using a kernel regression-based statistical downscaling method at 1.0°×1.0° spatial resolution was acquired from the Urban Climate Downscaling Portal (http://ucdp.icwar.iisc.ac.in/data/) (Kannan and Ghosh 2013; Salvi and Ghosh 2013).
Canadian Center for Climate Modeling and Analysis, Canada
5
3
CNRM-CM5
Centre National de Recherches Meteorologiques Coupled Global Climate Model, version 5
National Center of Meteorological Research, France
1.4 ×1.4
10
4
MPI-ESM-LR
Max Planck Institute Earth System Model, low-resolution
Max Planck Institute for Meteorology, Germany
1.88 ×1.87
3
5
MPI-ESM-MR
Max Planck Institute Earth System Model, medium-resolution
3.2. Trends and ETCCDI extreme indices computation
The CMIP5 model data were extracted for the Gujarat region to develop climatology for seasonal and the entire period. A multi-model mean (MMM) was calculated to observe the models diversity and variability in projecting the maximum/minimum temperature and extreme indices. However, the use of the optimal number of GCMs required to compute MMM is yet to be addressed therefore, all five-model data (mean values) were utilized for the same (Godonou et al. 2026). The Expert Team on Climate Change Detection and Indices (ETCCDI) has developed 16 core temperature extreme indices (http://etccdi.pacificclimate.org/index.shtml). These indices are widely used for monitoring, detection and attribution of temperature extremes at hourly, daily, monthly and annual scales (Jibhakate et al. 2023). These indices capture extreme events at different frequencies, intensities, and durations, even for future climate scenarios (You et al. 2018; Xu et al. 2019; Alexander et al. 2020). In this study, we have computed 7 ETCCDI (Table 2) defined extreme temperature indices using daily maximum and minimum data for selected CMIP5 model ensembles under RCP4.5 and RCP8.5 scenarios using the RclimDex software package. A trend and significance test was employed using a non-parametric Sen’s slope estimator and Mann-Kendall (MK) test at a 0.05% significance level. The Root Mean Square Error (RMSE) was also computed to evaluate the model performance (inter-comparison of models with multi-model mean) (See supplementary material section 1).
Table 2.ETCCDI-defined temperature indices computed in the study. The indices were calculated on an annual basis.
S.N.
ID
Indices name
Description
Unit
1
CSDI
Cold Spell Duration Index
Annual count with at least 6 consecutive days of min temperature < 10th percentile
Days
2
WSDI
Warm Spell Duration Index
Annual count with at least 6 consecutive days of max temperature > 90th percentile
3
TX10p
Cool days
Percentage of days when daily max temperature < 10th percentile
%
4
TX90p
Warm days
Percentage of days when daily max temperature > 90th percentile
5
TN10p
Cool nights
Percentage of days when daily min temperature < 10th percentile
6
TN90p
Warm nights
Percentage of days when daily min temperature > 90th percentile
7
DTR
Diurnal temperature range
Monthly mean difference between daily max and min temperature
°C
4. Results and Discussion
4.1. Projected trends in temperature climatology
A significant change in maximum and minimum temperature has been projected in RCP8.5 as compared to RCP4.5 scenarios, a similar observation made by Song et al., 2021. All models show, moreover a similar result, except for CNRM-CM5, which is likely to project lower maximum temperature in both RCPs (Figures 1 and 2). Despite this, all model simulations do not deviate much from the MMM and exhibit significant trends, except MPI-ESM-LR in the post-monsoon season (see Tables S1 and S2 of the supplementary file). Seasonal analysis shows that the projected minimum temperature from the BNU-ESM model seems to have a notable deviation from the MMM in the monsoon season in both scenarios. Notably, under RCP4.5, a significant decreasing trend in BNU-ESM was projected (Table 3). In both RCP scenarios, significant trends are projected in minimum temperature across all seasons, although the increase in RCP4.5 is less pronounced except in the winter season, in which temperature rises from 18.2°C to near 21°C (Figures 3 and 5). Furthermore, a projected change of >2.7°C (20-year average) was noticed in minimum temperature by the end of the 21st century.
Fig. 1.Temporal variation in minimum temperature for the period 2025-2099.Full sizeFig. 2.Temporal variation in maximum temperature for the period 2025-2099.Full sizeFig. 3.Seasonal variation in minimum temperature under RCP4.5 for the period 2025-2099.Full size
Table 3.Minimum temperature seasonal Sen’s slope values. All models show a significant value except for MPI-ESM-LR in RCP4.5 scenario.
Models
Pre-monsoon
Monsoon
Post-monsoon
Winter
RCP4.5
RCP8.5
RCP4.5
RCP8.5
RCP4.5
RCP8.5
RCP4.5
RCP8.5
BNU-ESM
0.01
0.03
-0.004
0.02
0.01
0.05
0.03
0.06
CanESM2
0.02
0.05
0.008
0.04
0.004
0.03
0.04
0.07
CNRM-CM5
0.01
0.03
0.01
0.04
0.007
0.03
0.03
0.06
MPI-ESM-LR
0.01
0.04
0.008
0.05
0.00004
0.05
0.02
0.07
MPI-ESM-MR
0.01
0.04
0.01
0.04
0.009
0.06
0.03
0.06
MMM
0.01
0.04
0.008
0.04
0.008
0.04
0.03
0.07
Bold values are significant at a 0.05% significance level
The time-series of maximum temperature also depicts similar trends to minimum temperature in the RCP4.5 scenario (Figure 4). During the pre-monsoon season, the BNU-ESM and canESM2 models show decreasing and significantly decreasing trends respectively. Consequently, the MMM for the pre-monsoon season shows a significant trend (0.003°C/year) at a 0.1% significance level. In the post-monsoon season, three models i.e. CNRM-CM5, MPI-ESM-LR and MPI-ESM-MR, possess non-significant increasing trends in maximum temperature in the RCP4.5 scenario (Table 4). However, in the higher-emission scenario all models show an increasing trend at a 0.05% statistical significance level. In the monsoon season, BNU-ESM and CNRM-CM5 models seem to have lower trends compared to the MMM, while in post-monsoon and winter seasons, the CNRM-CM5 model simulation has projected lower maximum temperature in RCP84.5 and 8.5 scenarios (Figure 6) as its RMSE value (see Figure S5- S8 of the supplementary file) is most deviated from the MMM values particularly in the post-monsoon season however, this does not indicate model superiority.
Fig. 4.Seasonal variation in maximum temperature under RCP4.5 for the period 2025-2099.Full sizeFig. 5.Seasonal variation in minimum temperature under RCP8.5 for the period 2025-2099.Full sizeFig. 6.Seasonal variation in maximum temperature under RCP8.5 for the period 2025-2099.Full size
Table 4.Maximum temperature seasonal Sen’s slope values for RCP4.5 and 8.5 scenario
Models
Pre-monsoon
Monsoon
Post-monsoon
Winter
RCP4.5
RCP8.5
RCP4.5
RCP8.5
RCP4.5
RCP8.5
RCP4.5
RCP8.5
BNU-ESM
-0.004
0.01
0.006
0.02
0.01
0.05
0.02
0.04
CanESM2
-0.005
0.01
0.02
0.05
0.01
0.04
0.03
0.05
CNRM-CM5
0.006
0.02
0.01
0.02
0.003
0.02
0.02
0.04
MPI-ESM-LR
0.009
0.03
0.01
0.05
0.003
0.05
0.02
0.05
MPI-ESM-MR
0.007
0.03
0.01
0.05
0.007
0.06
0.02
0.06
MMM
0.003*
0.02
0.01
0.04
0.01
0.04
0.02
0.05
Bold values are significant at a 0.05% significance level *Significant at 0.1% significance level
The highest increase in mean maximum temperature was projected during the post-monsoon months, where we could witness a rise of >4.66°C (MPI-ESM-MR) by the end of the 21st century if the current rate of change in emissions persists. Spatially, the highest rise in minimum temperature could be seen in the south-western and parts of the southern region of Gujarat respectively (Figures 7 and 9). However, we did not observe any significant differences in the spatial distribution of minimum temperature except in the intensity under both scenarios. This would also be attributed to the poor global model resolution in accurately simulating the data at a regional scale (Dileepkumar et al. 2021). Conversely, the majority of the eastern part depicted higher maximum temperature in all model observations except CNRM-CM5 in both scenarios (Figures 8 and 10). The western part will experience an average temperature of <34.5°C and <35.5°C in the RCP4.5 and RCP8.5 scenarios respectively.
Fig. 7.Spatial variation in minimum temperature under RCP4.5. All models show a higher temperature in southern and western part of Gujarat.Full sizeFig. 8.Spatial variation in maximum temperature under RCP4.5. Grids in south-eastern part of the region posses a higher maximum temperature.Full sizeFig. 9.Spatial variation in minimum temperature under RCP8.5. All models show a comparative higher temperature grids in southern and western part of Gujarat.Full sizeFig. 10.Spatial variation in maximum temperature under RCP8.5. CNRM-CM5 depict lowest temperature over entire region as compared to other models.Full size
4.2. Temperature extreme indices
The changes in extreme indicators are significant except for DTR, which shows a non-significant trend in some models (BNU-ESM, MPI-ESM-LR, MPI-ESM-MR) under RCP4.5. In RCP8.5, the MMM of DTR depicts a decreasing trend with a change of approximately 0.6°C by the end of the century (Figure 12). The modest change in DTR is determined by the similar rate of change in minimum and maximum temperature. The spatial patterns are consistent across all models for both scenarios, as the maximum change is expected in the middle to the eastern part of Gujarat, where a difference of >3°C has been projected especially in MPI-ESM-LR however, this change is characterised by a comparatively greater increase in minimum temperature (Figure 16).
The cold spells are projected to decrease from 53 days in a year to 6 days in RCP4.5, and even more significantly from 84 days to just 1 day in the higher emission scenario (Figure 11). The majority of grids show an average of less than 16 days of cold spells in CNRM-CM5, MPI-ESM-LR and ESM-ESM-MR model projections in both scenarios whereas BNU-ESM and can-ESM2 depict higher cold spells (>16 days) for the entire region in RCP8.5 (Figure 15). Similarly, warm spells show a likely pattern with cold spells perhaps canESM2 also shows a lower (<16 days) warm spell duration in both scenarios (Figure 21). Overall, the CSDI and WSDI exhibit significant decreasing and increasing trends respectively, a similar observation was also made globally by Agnihotri et al., 2018; Alexander and Arblaster, 2017. In conclusion, the areas which experience extreme cold spells will also have warm spells of similar frequency and duration.
Fig. 11.Temporal change in CSDI and WSDI for RCP4.5 and RCP8.5 scenario.Full sizeFig. 12.Temporal change in DTR for RCP4.5 and RCP8.5 scenario for the period 2025-2099.Full size
The warm spells are increasing at a greater rate in RCP8.5 compared to the lower scenario (0.22 days per year for RCP4.5 versus 0.57 days per year for RCP8.5). However, the trends are significant in both scenarios (Figure 22). The mean warm spell duration in higher scenarios could reach up to 107 days by the end of the 21st century however, the maximum days are projected in MPI-ESM-MR (162 days) simulations (Figure 11). The CSDI and WSDI possess an inverse relationship (The initial decade will have more cold and fewer warm spells while this scenario will be reversed by the end of the century) with each other which further affects the cool and warm nights and days.
Similarly, cool nights/days and warm nights/days are showing significant decreasing and increasing trends respectively (Figures 13 and 14). The cool and warm nights are changing at a rate of 0.17 and 0.38 percentage points per year whereas cool and warm days are changing at rates of 0.13 and 0.34 percentage points per year (Figure 22). From the rate of change, we projected that the largest changes in warm and cold days/nights appear in higher emission scenario as also reported by Dash and Mamgain, 2011; Khan et al., 2020; Lee et al., 2020. Though, some researchers also reported that uncertainties in simulating the cold extremes with CMIP5 simulations are 50% higher than in warm extremes (Kharin et al., 2013; Panjwani et al., 2013). However, in the Indian context, the cold extremes are more closely correlated with the Indian Summer Monsoon Index (ISMI) (Rehana et al. 2022). Therefore, the projected cold/warm extremes over Gujarat will be more influenced by the atmospheric aerosols and their optical properties, anthropogenic warming and El Nino/La Nina patterns (Gadgil and Dhorde 2005; Kothawale et al. 2012; Panda et al. 2014).
Fig. 13.Temporal change in TN10p and TN90p for RCP4.5 and RCP8.5 scenario. The percentage of warm nights will significantly increase by the end of the century as depicted by TN90p.Full sizeFig. 14.Temporal change in TX10p and TX90p for RCP4.5 and RCP8.5 scenario. The percentage of cool and warm days will significantly decrease and increases as depicted by TX10p and TX90p respectively.Full sizeFig. 15.Spatial distribution of CSDI in RCP4.5 and RCP8.5 scenario. Highest cold spells are projected in BNU-ESM.Full sizeFig. 16.Spatial distribution of DTR in RCP4.5 and RCP8.5 scenario.Full size
The spatial patterns of cold and warm extremes are much more diverse in each model for both scenarios. The percentage difference in cool nights is not significant (~0.5%) in both scenarios. However, in RCP8.5, the southern part seems to have higher intensity as depicted by BNU-ESM and MPI-ESM-MR model projections (Figure 17). For warm nights, a similar pattern has been projected in all models except canESM2 in RCP4.5. Furthermore, a relative increase in the percentage of warm nights (especially in southern Gujarat) has been noticed in all model simulations except MPI-ESM-LR in the RCP8.5 scenario (Figure 18). The cool days have a scattered spatial pattern. Models such as MPI-ESM-LR and MPI-ESM-MR depict a more or less similar intensity of cool days in both scenarios. Whereas canESM2 and CNRM-CM5 yield no significant difference in RCP4.5 and 8.5 however, BNU-ESM is expected to experience a slightly higher percentage of cool days (Figure 19). For warm days, CNRM-CM5 and MPI-ESM-MR models show a similar intensity of warm days in RCP4.5 while MPI-ESM-MR depicts no significant change in both scenarios. Furthermore, all models show slightly intensified warm days (a change of >0.4%) except MPI-ESM-LR in entire Gujarat (Figure 20).
Fig. 17.Spatial distribution of TN10p in RCP4.5 and RCP8.5 scenario.Full sizeFig. 18.Spatial distribution of TN90p in RCP4.5 and RCP8.5 scenario. Highest percentage of cool nights is projected in canESM2 as compared to other models.Full sizeFig. 19.Spatial distribution of TX10p in RCP4.5 and RCP8.5 scenario. MPI-ESM-LR and MPI-ESM-MR projects higher percentage of cool days in both scenarios over Gujarat.Full sizeFig. 20.Spatial distribution of TX90p in RCP4.5 and RCP8.5 scenario. Lowest percentage of warm days are projected by MPI-ESM-LR on both scenarios.Full sizeFig. 21.Spatial distribution of WSDI in RCP4.5 and RCP8.5 scenario. Highest warm spells (>20) are projected by BNU-ESM when compared to other models.Full sizeFig. 22.Projected trends in extreme indices in RCP4.5 (above) and RCP8.5 (below) scenarios. Values depict the rate of annual change in extreme indices in all models and MMM. Bold values are significant at a 0.05% significance level. The units of change for duration (CSDI/WSDI), percentile (TN10p, TN90p, TX10p, and TX90p) and absolute indices (DTR) are days per year, percentage points per year and °C per year respectively.Full size
All models exhibit a range of variability in simulating temperature change and extreme indices over the Gujarat region, as also demonstrated by Gupta et al., 2020. The variability in time-series data among models is a reflection of limitations in accurately simulating the climate data without identifying the influence of external factors at a regional scale. However, all extreme indices have performed comparatively well in both scenarios except CSDI and WSDI (see Figure S13 and S14 of the supplementary file).
5. Conclusion
The study examined temperature climatology and extreme temperature indices for the period 2025-2099 using selected CMIP5 model simulations. The models BNU-ESM and CNRM-CM5 show greater variability in estimating the temperature indices, particularly under higher emission scenarios. In contrast, canESM2, MPI-ESM-LR and MPI-ESM-MR performed well in simulating both minimum and maximum temperatures at an annual and seasonal level. A detailed inter-comparison of models with MMM for each extreme indices for both scenarios has also been computed (see Tables S1-S4 and S6-S7 of the supplementary file). The MMM warm days and nights percentage will increase to over 29% and 35.6% (of total days in a year) respectively. Similarly, cold days and nights are expected to decrease to 1-3% in some models. This indicates a significant shift in the intensity of the trends in the time series from the mid to end of the 21st century.
The spatial variability of extreme trends is significantly greater than the temporal changes projected. Additionally, the variation in maximum and minimum temperature trends exhibits similar spatial patterns across both emission scenarios. However, multiple studies have reported that the current rate of GHGs emissions, Indian summer monsoon trends, global circulations and energy transfer from sea to land or vice-versa will determine the future temperature change intensity over Gujarat. The study conducted in the era of changing climate over the diversified Gujarat region holds valuable insights into the projected temperature extreme indices variability under various global emission scenarios. With the help of projected extreme indices, one can also estimate their implications for sectors such as agriculture, health, socioeconomics and the environment.
Declarations
Author contributions.Sandeep Kumar: Conceptualization, Methodology, Data curation, Formal analysis, Validation, Writing: Original draft, review and editing
Funding.No specific funding has been received for and during the preparation of this manuscript
Conflicts of interest.Author declares that he does not have any financial or non-financial conflict of interests
Data availability.Data and Python code used for the analysis may be provided by the author upon reasonable request
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How to cite this article
Kumar, S. (2026). 'Projected temperature extremes variability assessment using CMIP5 multi-model ensembles over Gujarat, India'. Global Decarbonisation, 2, pp. 228-251. https://doi.org/10.65582/gd.2026.012
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