Characterization of Heatwave Patterns and Its Long-run Predictions Using CMIP6 Model in Western and North-Western Climatic Zones of Bangladesh

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Khalid Hasan, Chow. M. Sarwar Jahan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4218891/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Globally, the hazards related to climate change effect such as tropical cyclones, storm surges, coastal flooding, river erosion, floods, droughts, heatwave, cold wave etc. are burning challenge that drastically effects people’s food security, health, ecosystems and society as a whole. In recent decade, rising trend of unusually prolong high temperature or heatwave episode is a burning concern. Present study area address the heatwave patterns in western climatic zone (E), and north-western climatic zone (D) in the north-western part of Bangladesh. Using historical maximum temperature (T MAX ) data during the period of 1996-2014, and model-projected T MAX data (2015- 2022) have collected. Moreover, two shared socio-economic tools such as SSP_245 and SSP_585 have used in present study along with projected data for the years of 2024–2050. The study uses heatwave alerts from the Bangladesh Meteorological Department (BMD) to classify heatwave days into four categories: mild, moderate, severe, and extreme. To improve accuracy, four general circulation models (GCMs) from the Coupled Model Inter-comparison Project phase 6 (CMIP6) have assessed following bias correction, and merged using an ensemble technique. The median has used to reduce extreme value sensitivity. According to the BMD criteria, the results show that severe and exceptional hot days have occurred often in recent years. This tendency could continue into the future, putting a sizable population at risk. The heatwave days are significantly more common under SSP_585 than they are under SSP_245, demonstrating the straightforward influence of human activity on heatwave regularity. To lessen the negative effects of heatwaves, fair and practical measures for preparing for and responding to them should be developed, and will be facilitated by this thorough analysis of heatwave projections. The results of present study highlight the urgency of taking immediate action to improve heat wave readiness. These study results are expected to enhance the accomplishment of the Sustainable Development Goals (SDG 3, SDG 11, and SDG 13) by guiding proactive steps to mitigate the consequences of severe heat wave days. Heatwave Maximum temperature CMIP6 Northwestern Climatic Zones Bangladesh Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction According to the UN International Strategy for Disaster Reduction (UNISDR et al. 2015), heatwave is the ten most deadly natural disasters in 2015, where South Asia stands in third in terms of death, and becoming one of the deadliest natural catastrophes with mortality rates rising in both industrialized and developing countries. But it is important to remember that heat-related deaths are mostly preventable. Many developed countries have built heat early warning systems in cities, and study has shown that these systems can save lives by improving readiness program (Ebi et al. 2004; McGregor et al. 2015). In Bangladesh, a country that experiences natural disasters as recurrent phenomenon, and among other the frequency and severity heatwaves as environmental hazards is expected to increase in coming years. Numerous yearly disasters such as tropical cyclones, storm surges, coastal flooding, river erosion, floods, and droughts, cause major losses in terms of human life, property, and land in the country (Uddin and Suravi, 2019). Bangladesh - a densely populated small area country has effected by regional variations in the waves of natural disasters and climate change (Al-Maruf et al., 2020). Although the country is extremely vulnerable to climate change phenomenon (Kreft et al., 2016), but it has lack of information regarding the projections of long-term heatwave patterns that will be expected to rise above current levels (Ripon and Al-Mamun, 2020). According to earlier studies, the temperature has increased by 0.50 °C with a rise in temperature of 0.30 °C during the period of last 20 years in the country (Ripon and Al-Mamun, 2020). The country is situated in the subtropical monsoon belt with diverse seasonal patterns and is divided into seven unique zones - the south-southeast (A), north-eastern (B), northern (C), north-western (D), western (E), south-western (F), and south-central (G) based on overall climate pattern (Rashid 1991). The northwestern (D) zone including Rajshahi, Chuadanga, Ishwardi, Bogra, and Dinajpur, and the western (E) zone in Rajshahi, about 1157 km 2 area have taken for present study (Figure 1). The study area is bordered by the Indian states of West Bengal with average maximum temperature is 37.1°C in the summer (March to June), and minimum temperature is 11.2°C in the winter (December to February) with average annual rainfall amount is 1467 mm. According to the Bangladesh Meteorology Department (BMD), Ishwardi has recorded as the hottest temperature (43 0 C) in the country in 2023. Moreover, the climatic zones D and E in the north-western part of country are worst victim of the natural calamities such as thunderstorms, floods, cold and heat waves, drought, riverbank erosion, water scarcity etc. (Karmakar, 2019). In recent years, these climatic zones have also become an ‘Urban Heat Island - UHI’ and the problem getting worse every year (Tanvir et al., 2019; Ripon and Al-Mamun, 2020) than other parts of the country because more vulnerable due to heat waves, high temperatures and low humidity. Moreover, the investigation on severe temperature patterns can yield important information about heatwave patterns in the western (E) and northwest (D) climatic zones of the country. Generally, heatwave predictions are made using General Circulation Models (GCMs), where global temperature changes under various climate change conditions. Nevertheless, further statistical methods are required to improve regional projection accuracy, particularly for extreme events (Nandi, S. and Swain, 2023). According to earlier research, northwest and west-central regions in Bangladesh are expected to face more increases in temperature, with future increases expected to exceed 5 °C by the end of the 21 st century under RCP_85 (Representative Concentration Pathway 85) compared to the reference climate (1981–2005). The average annual mean maximum temperature in the country is predicted to rise by 1.9 °C and 4.0 °C by the end of present century under the RCP_45 and RCP_85 situations, respectively. Similarly, by the end of this century, it is predicted that the annual mean lowest temperature averaged in the country will rise by 2.1 °C and 4.0 °C, respectively, under RCP_45 and RCP_85 situations. Under all emission conditions, the average annual mean temperatures in the country likewise exhibit a significant increase in comparison to the reference climate (Bosu et al 2021). It is predicted that heatwaves will grow more intense and long-run in many parts of the world (Cowan et al. 2014). It is noteworthy that, RCP_45 and RCP_85 among other RCP situations are more frequently cited in the scholarly literature (Nandi and Swain, 2023). To address the limitations found in CMIP5 (Coupled Model Intercomparison Project Phase 5) model and to suit the changing needs of the climate research community, a new version of the CMIP6 model has recently introduced (Stouffer et al. 2017). The model responses has hampered by CMIP5 with severe technical flaws including difficulties in minimizing systemic biases, weighing the advantages and disadvantages of various mitigation techniques, and precisely measuring radiative forcing. At present CMIP6 model has adopted to overcome these limitation by using the Shared Socioeconomic Pathway (SSP). According to Eyring et al. (2016), these situations prioritize resolving model biases and offer a more coordinated depiction of land surface processes and atmospheric aerosols. They also offer solutions for predicting variability on timeframes more than year. The yearly mean temperature in South Asia is predicted to rise by 2.1 °C and 4.3 °C, respectively, under the model of the SSP_245 and SSP_585 situation (Das and Umamahesh 2022). It is noteworthy to emphasize that these SSP situations are consistent with RCP_45 and RCP_85 predictions under the CMIP5 experiment. Moreover, few studies have been carried out with CMIP6 to predict future changes in heatwave frequency globally (Plecha and Soares 2020; Kim et al. 2020), and also in Bangladesh to date considering data prioritizing meteorological circumstances. Additionally, it is imperative to evaluate their ability to produce accurate meteorological projections in future risk assessments. The Coupled Model Inter-comparison Project Phase 6 (CMIP6) is a collaborative global initiative focusing on climate phenomenon, which incorporates advanced climate models and standardized experiments to enhance past, present, and future climate changing and understanding. The exploration of future scenarios has performed by the SSPs software tool in five labelled scenarios. Here the SSP_119 and SSP_126 represent the low-emission trajectories with stringent climate policies, aiming to limit global warming to 1.5 °C and below 2 °C. The SSP_245 reveals an intermediate emissions situation with moderate climate policies, and the SSP_370 indicates medium to high-emission situation with limited policies that projects substantial global warming. The SSP_585 represents a high-emission situation with no or limited policies, resulting in the highest radiative forcing with significant global warming. However, SSPs provide a framework for exploring diverse future scenario assessing potential climate impacts; crucial for informing climate science assessments; and understanding the range of possibilities and uncertainties associated with different socio-economic pathways . In present study, SSP245 and SSP585 scenarios referred to as ‘SSP_245’ and ‘SSP_585’ have used to characterize medium and high emission situations respectively. It's crucial to remember that the highest temperatures happen during the day, that of the lowest at night. But the main approach to identify heatwaves is to consider at the maximum daily temperature ( T MAX ), as the goal of present study is to measure the number of heatwave days to make the results easier for interpretation. The criterion of BMD is given in Table 2. The heat waves have been occurring more frequently in the recent past (Das and Umamahesh 2022; Patel et al. 2022), and also been considering as personal experiences, physiological alterations, social and political occurrences etc. The extended periods of unusually high temperatures as pointed out by meteorologists and climatologists that results the growing rate of morbidity and mortality rates, and has considered emergencies by legislators. The heat waves happens during the pre-monsoon season when the highest temperature reaches to 36 °C. Depending on the severity and length of the heatwave duration, the BMD has classified as mild heat waves (36–38 0 C), moderate heat waves (38–40 0 C), severe heat waves (40–42°C), and very severe or extreme heat waves (>42°C). Keeping all these concerns in mind, attempt has made in the present study to examine the patterns of heatwaves in the several cities in the western climatic zone (E), and north-western climatic zones (D) in the western part of Bangladesh, with an aims to utilize an ensemble technique to combine four GCM models to improve the accuracy of the results; characterize heatwaves using approaches based on BMD heatwave warnings and evaluating their historical frequency using observed data; and forecasting and analyzing future heatwave characteristics using four CMIP6 model of GCMs and comparing these projections with recorded conditions. Datasets and Methodology A gridded observational dataset covering maximum temperature ( T MAX ) with a daily temporal resolution and a geographical resolution of 1°×1° has been provided by the BMD during the period of 1981-2022. The BMD carefully measured ground-based observations from the stations spread across the study area to create this gridded dataset, which is subjected to stringent quality assurance processes. In the context of present study, the gridded T MAX dataset of BMD has taken into consideration during the period of 1996-2022 due to its alignment with the historical data derived from Global Climate Models (GCMs). The details of these GCMs models, including their spatial resolutions, and affiliated institutions have given in Table 1. To study present heatwave events, four GCM models that best fit as base for the climate of Bangladesh (Kamruzzaman et al., 2021) have selected, and SSP_245 and SSP_585 models are used as two emission situations. The Earth System Grid Data Portal (https://esgf-node.llnl.gov/search/cmip6/) has provided simulated data from four GCMs in the CMIP6 dataset of two SSPs. The long run time period (1996-2022) has taken for present study. The historical Tmax in model has used to extract the historical Tmax for the years 1996–2014, and the projected Tmax for SSP_245 and SSP_585 have used to obtain the historical Tmax for the period of 2015–2022. Consequently, two sets of historical Tmax are obtained. There exists differences between the historical data from the model and the observed data from the BMD, which are described to load factors, parameterization, resolution, and climate variability. Here the additive scaling method has used to address these biases. This statistical technique makes it easier for historical outcomes to match model-simulated results, and ensures a smooth transition into projected values for the future (Ullah, S. et al., 2022). An additive scale between BMD observed and simulated Tmax during the time frame of 1996–2022 has calculated for each daily maximum temperature of model as follows: T SIMULATED - T OBSERVED = T ADDITIVE_SCALING Moreover, the future estimation of Tmax in model during period of 2024–2050 has updated to include T ADDITIVE_SCALING T ADDITIVE_SCALING + T MAX_MODEL_PROJECTED = T MAX_PROJECTED The future projected Tmax for four models have obtained after correcting of biases. The four models are then combined using an ensemble technique, which can produce results that are more accurate than those obtained from using a single model (Riccio, 2007). Since the median is less sensitive to extreme events, so it has chosen in ensemble strategy of the present study. A gridded observational dataset covering maximum temperature ( T MAX ) with a daily temporal resolution and a geographical resolution of 1°×1° has been provided by the BMD during the period of 1981-2022. The BMD carefully measured ground-based observations from the stations spread across the study area to create this gridded dataset, which is subjected to stringent quality assurance processes. In the context of present study, the gridded T MAX dataset of BMD has taken into consideration during the period of 1996-2022 due to its alignment with the historical data derived from Global Climate Models (GCMs). The details of these GCMs models, including their spatial resolutions, and affiliated institutions have given in Table 1. To study present heatwave events, four GCM models that best fit as base for the climate of Bangladesh (Kamruzzaman et al., 2021) have selected, and SSP_245 and SSP_585 models are used as two emission situations. The Earth System Grid Data Portal (https://esgf-node.llnl.gov/search/cmip6/) has provided simulated data from four GCMs in the CMIP6 dataset of two SSPs. The long run time period (1996-2022) has taken for present study. The historical Tmax in model has used to extract the historical Tmax for the years 1996–2014, and the projected Tmax for SSP_245 and SSP_585 have used to obtain the historical Tmax for the period of 2015–2022. Consequently, two sets of historical Tmax are obtained. There exists differences between the historical data from the model and the observed data from the BMD, which are described to load factors, parameterization, resolution, and climate variability. Here the additive scaling method has used to address these biases. This statistical technique makes it easier for historical outcomes to match model-simulated results, and ensures a smooth transition into projected values for the future (Ullah, S. et al., 2022). An additive scale between BMD observed and simulated Tmax during the time frame of 1996–2022 has calculated for each daily maximum temperature of model as follows: T SIMULATED - T OBSERVED = T ADDITIVE_SCALING Moreover, the future estimation of Tmax in model during period of 2024–2050 has updated to include T ADDITIVE_SCALING T ADDITIVE_SCALING + T MAX_MODEL_PROJECTED = T MAX_PROJECTED The future projected Tmax for four models have obtained after correcting of biases. The four models are then combined using an ensemble technique, which can produce results that are more accurate than those obtained from using a single model (Riccio, 2007). Since the median is less sensitive to extreme events, so it has chosen in ensemble strategy of the present study. Results and Discussions For the observed historical 27 years (1996-2022) data of daily maximum temperatures ( T MAX ) for each of the five cities (Figure 2) , it is revealed that Dinajpur and Bogra vary from 23 °C to 24 °C in the winter to 35 °C to 37 °C in the summer having pleasant weather conditions. On the other hand, that of T MAX varies from 22 °C – 23 °C during winter days to 35 °C – 41 °C during summer days in Rajshahi, Chuadanga, and Ishwardi representing unusually hightemperature, especially in the summer months due to areal strong solar radiation, high evaporation, and limited rainfall. Based on the BMD criterion, the number of annual heatwave days for the historical period in the five cities are presented in Figure 3(a-d). Accordingly, heat wave phenomenon in the study area has divided into categories as mild, moderate, severe, and extreme. It is observed that historically the area experiences a number of moderate heatwaves days with rising trend especially after 2010. Among the studied cities, Rajshahi stands with the highest number of moderate heatwave days, notably in recent decade (2010-2022) (Figure 3a) representing typically 30 and 50 days every year along with nearly largest (75 days) in2022. Similarly, Chuadanga and Ishwardi demonstrate essentially identical trends (Figure 3a), with an average number of 20 to 40 mild heatwave days each year. Although there has been a rising trend of heatwave days in the last few years in Bogra and Dinajpur, but recent data shows that comparatively less number of mild heat waves where typically annual counts range from 10-20 days (Figure 3a). In present study area, moderate heatwave days occur annually where Rajshahi has the highest frequency and occurs in 5-20 days with exceptionally 25 days in 2012; however, in subsequent years, this number has decreased (Figure 3b). Similarly Chuadanga and Ishwardi also have 5-15 mild heatwaves days annually, though their frequency has decreased in recent time (Figure 3b). On the other hand, Bogra and Dinajpur normally have 0-5 days with moderate heatwaves days with exceptional rising trend in days in 2014 of nearly 8 days (Figure 3b), andthe frequency of mild heatwaves has declined in the last few years. It is visible that a discernible rise in the frequency of severe heatwave days has observed in Chuadanga, Rajshahi, and Ishwardi over time in recent decade especially after 2008, whereas Chuadanga and Rajshahi have the highest number of severe heatwave days in 2014 which has followed in subsequent decrease in frequency significantly (Figure 3c). There have fluctuation with moderately frequent severe heatwave days in Ishwardi with duration of 1-5 days. In contrast, Bogra and Dinajpur have witnessed few cases of severe heatwave days with no report of severe heatwave days after 2013 (Figure 3c). The long-run historical annual occurrences of heatwave days in the cities of the study area such as Rajshahi, Chuadanga, and Ishwardi have experienced the most extremities (Figure 3d) . After some specific time period, no city has experienced extreme heatwaves days except Chuadanga which encounters single day of extreme heatwaves in both 2005 and 2014 (Figure 3d) . Apart from these isolated occurrences, no other cities has observed extreme heatwaves days in the past couples of years. In the study area, five cities have shown a rise in the overall frequency of heat wave days over time, particularly after 2010. The cities such as Bogra and Dinajpur are comparatively less prone to heatwaves days than that of Rajshahi, Chuadanga, and Ishwardi. The assessment of upcoming heat waves is essential, and the dependability of dataset is critical. The variety of GCM-based simulations from the CMIP6 study has used in present study, and it is crucial to contrast their T MAX projections with the observed data. Consequently, using BMD data, the chosen models (ACCESS-CM2, ACCESS-ESM1-5, CanESM5 and INM-CM5-0) are assessed for T MAX forecast across the long run historical data. To improve accuracy, differences between the model historical data and observed BMD data are found and fixed. The four models are then combined using an ensemble technique to yield findings that are more accurate as if only one model will be used. To be consistent with the previous 27-year timeframe, the anticipated period has considered as 2024-2050. The number of heatwave days annually, based on BMD criteria for SSP_245 and SSP_585 situations from CMIP6 temperature projections in five cities in the study area during this period is illustrated in Figure 4(a-e) and Figure 5(a-e) respectively, and the total number of heatwave days are summarized in Table 2 . It is obvious from Figure 4(a-e) and Figure 5(a-e) that these cities will experience a significantly different number of heatwave days in the future than they did during the historical period. The SSP_245 situation predicts that during the period of 2024-2050, there will be number of heat days such as 614, 597, 526, 340, and 325 of mild nature in Rajshahi, Chuadanga, Ishwardi, Bogra, and Dinajpur, respectively. It is predicted that there will be moderate heatwaves over these cities for 225, 217, 171, 57, and 69 days, respectively. The forecasted dates for severe heatwaves are 85, 75, 53, 10, and 5 days respectively, and that of extreme heatwaves are 31, 26, 15, 1, and 0 days respectively. Similar to this, the SSP_585 situation projects that during the years 2024–2050, there will be 763, 665, 637, 405, 412 days of mild heatwaves over Rajshahi, Chuadanga, Ishwardi, Bogra, and Dinajpur, and 289, 289, 222, 111, and 95 days of moderate heatwaves. Projected durations of severe heatwaves are 121, 75, 69, 18, and 14 days, respectively. There will be extreme heat waves of 43, 36, 28, 2, and 2 days, respectively. The highest anticipated number of mild, moderate, severe, and extreme heatwave days in a year for Rajshahi under the SSP_245 situations 47, 21, 13, and 9 days, respectively during the period of 2023–2050. Chuadanga has 32, 19, 15, and 11 days, in that order; and 41, 17, 8, and 5 days, respectively for Ishwardi. Bogra has 24, 10, 3, and 1 days, in that order, and 38, 10, 2, and 0 days, respectively, for Dinajpur. In similar manner, the highest anticipated number of mild, moderate, severe, and extreme heatwave days in a year in Rajshahi for the SSP_585 situations is 63, 42, 29, and 12 days, respectively. Chuadanga has 55, 34, 13, and 7 days in that order. 52, 42, 16, and 10 days, respectively, for Ishwardi. 31, 13, 4, and 1 day for Bogra in that order. It is 40, 14, 3, and 1 day for Dinajpur respectively. Table 3 represents a comparison of predicted heatwave days for the future with historical data. The results of this study highlight the rising risks that heatwaves pose in the future due to climate change effect, especially in Rajshahi, Chuadanga, and Ishwardi. Table 2 shows that while severe and strong heatwaves are predicted to grow significantly in all five cities, and mild and moderate heatwave days are predicted to decline. This means that there will be more heatwave days in the future time, which highlights the way of urgently strong legislation has to be formulated to combat the growing effects of heatwaves days in the present studied cities. Heatwave days are associated with serious health disorders like diabetes mellitus morbidity (Song et al. 2021), schizophrenia (Tang et al. 2021), infectious diarrhea (Liang et al. 2021), and mental disorders (Yoo et al. 2021). In addition, heatwaves days pose a significant risk to public health due to rising ambient temperatures and extreme conditions. Furthermore, Ran et al. (2020) has shown that there is a higher chance of flash droughts in areas where heatwaves days occur frequently. (Singh et al. 2021; Swain et al. 2022a). Recent studies have repeatedly shown that extreme weather is becoming more frequent and severe due to climate change, especially in metropolitan areas. Thus, it is essential to take preventative actions to learn the negative impact of intense heatwaves days. The United Nations General Assembly has developed 17 Sustainable Development Goals (SDGs) in 2015 (UN DESA 2016). More sustainable future, three specific of SDGs in line with current study are: SDG 3 (ensuring healthy lives and promoting well-being for all at all ages); SDG 11 (making cities inclusive, safe, resilient, and sustainable); and SDG 13 (taking urgent action to combat climate change and its impacts) (Swain et al. 2022b, 2022d). The enhancement of cities' resilience and adaptability to climate hazards and disasters, especially in developing countries, is the unifying target of these SDGs goals (Guptha et al. 2021, 2022). The study urgently highlight the need for planning of heatwave preparedness. In this context, proactive interventions should include planting trees, implementing smart growth strategies, installing green roofs, creating urban inland water bodies, and using materials with high albedo on the outside of buildings (Patel et al. 2022b). Additionally, the SSP5-8.5 situation considers much higher number of heatwave days than that of SSP2-4.5 in every city in the study area, and strongly demands strong climate policies to be developed, especially for urban centers in the western part of Bangladesh. Conclusion The frequency of extreme weather events like heatwave is becoming a growing concern as a climate change effect. Present study aimed to assess the features of heatwaves in five cities such as Rajshahi, Chuadanga, Ishwardi, Bogra, and Dinajpur in the northwestern part of country that are particularly sensitive to heatwave days. The study has included forecasts of heatwaves for the period of from 2024-2050 considering the long run historical data from 1996-2022. The heatwave alerts of BMD have used to identify heatwaves. Four GCMs from CMIP6 have examined for bias correction in their maximum daily temperature ( T MAX ) outputs to evaluate temperature outputs. The four models are then combined using an ensemble technique to improve accuracy. This method has made it easier to create temperature predictions for the future that correspond to socioeconomic pathways (SSP_245 and SSP_585). Finally it may reveal that: In comparison to the historical (1996-2022) T MAX , Bogra and Dinajpur shows comfortable weather with fewer days of heatwaves, meeting the standards set by the. On the other hand, out of the five cities Rajshahi, Chuadanga, and Ishwardi have the most heatwave days. The BMD heatwave criteria are applied for historical as well as predicted Tmax of GCMs model data. To improve the accuracy of the results, four models from the CMIP6 experiment have taken, subjected to bias correction, and then combined using an ensemble technique. The anticipated future shows a notable increase in the number of heatwave days. Out of the five cities, Rajshahi, Chuadanga, and Ishwardi are expected to be the most severely affected according to the CMIP6 model that is being used. In comparison to SSP 2–4.5, the frequency and intensity of heatwave days will be more noticeable in all cities under SSP 5–8.5, and demonstrates the human activity directly contributes to the rise in heatwaves. The results highlight the urgency of taking immediate action to improve heatwave readiness. These study results are expected to enhance the accomplishment of sustainable development goals (SDG 3, SDG 11, and SDG 13) by guiding proactive steps to mitigate the consequences of severe heatwave days. Declarations Author Contribution Author 1 - Rayhan Ahmad: Conceptualization, Formal analysis, Investigation, Data management, software, writing-original draft.Author 2 - Md. Khalid Hasan: Methodology, software, Visualization, investigation.Corresponding Author - Chow. M. Sarwar Jahan: Review and editing. 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J. Disaster Adv. 13 (9). Tanvir, Hossain, Humayain, Kabir Md., Mahmud, Khan Mohammed Bayezid, 2019. Flood vulnerabilities, impacts and their coping techniques in Island Areas of Muladi Upazila in Barishal District, Bangladesh. Disaster Adv. 12 (8), 41–59. Uddin, M.S., Suravi, R.H., 2019. The rise of a new disaster in Bangladesh: Analysis of characteristics and vulnerabilities of lightning during March to September 2018. In: Proceedings on International Conference on Disaster Risk Management, Dhaka, Bangladesh, January 12-14. UN DESA (2016) The Sustainable Development Goals Report 2016. United Nations Publications, United Nations Department of Economic and Social Affairs. https://doi.org/10.18356/3405d09fen Tables Table 1 GCMs from CMIP6 used in this study. Sl. No. Models Institution Resolution 1 ACCESS-CM2 Australian Community Climate and Earth-System Simulator 1.88° × 1.25° 2 ACCESS-ESM1-5 Australian Community Climate and Earth-System Simulator 1.88° × 1.25° 3 CanESM5 Canadian Earth System Model 2.8° × 2.8° 4 INM-CM5-0 Institute for Numerical Mathematics, Russia 2°×1. 5° Table 2 BMD-classified heat waves. Heat Wave Maximum Temperature Range Mild Heat Wave 36-38°C Moderate Heatwave 38-40°C Severe Heatwave 40-42°C Extreme Heatwave >42°C Table 3 Total number of heatwave days throughout the historical period (1996–2022) based on BMD data and future situations (SSP245 and SSP585 from 2024 to 2050 year) based on CMIP6 (GCMs data by using BMD criteria for five cities). City name: Rajshahi Mild Moderate Severe Extreme Historical Period (1996-2022) 950 310 88 8 SSP245 (2024-2050) 614 225 85 31 SSP585(2024-2050) 763 289 121 43 City name: Chuadanga Mild Moderate Severe Extreme Historical period (1996-2022) 851 247 71 8 SSP245(2024-2050) 597 217 75 26 SSP585(2024-2050) 665 289 75 36 City name: Ishwardi Mild Moderate Severe Extreme Historical period (1996-2022) 823 191 42 2 SSP245(2024-2050) 526 171 53 15 SSP585(2024-2050) 637 222 69 28 City name: Bogra Mild Moderate Severe Extreme Historical period (1996-2022) 373 42 3 0 SSP245(2024-2050) 340 57 10 1 SSP585(2024-2050) 405 111 69 2 City name: Dinajpur Mild Moderate Severe Extreme Historical period (1996-2022) 350 48 4 0 SSP245(2024-2050) 325 69 5 0 SSP585(2024-2050) 412 95 14 2 Additional Declarations No competing interests reported. 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Khalid Hasan","email":"","orcid":"","institution":"University of Rajshahi","correspondingAuthor":false,"prefix":"","firstName":"Md.","middleName":"Khalid","lastName":"Hasan","suffix":""},{"id":288888998,"identity":"d3c5fd95-8f63-4c12-a339-2070b3fa2c0f","order_by":2,"name":"Chow. M. Sarwar Jahan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYHAD5gYgYcPAIEG8FkaQljTStRwmrEW+/ewziZ97GOz62RsbH35tO5/YP7v54AOGGptoXFoMzqSbSfY8Y0ie2XOw2Vi27XbijDvHkg0YjqXlNuDSwpDGdoPnAEOywY3ENmlJoJaGGzlmEowNh3Fqke9/xnbzD1CLPUTLucT5hLQw3Ehjuw20xc5AIrFN8mPbgcQNhLQY3HjG/lvmAEOCxBmgXxjOJRtvvJGWbJCAxy/y/WnMhm8OMNjztzcffPijzE523o3kgw8+1NjgdhgE/E8EKWDmZWNwBKtMwK8cDOxBBOOPPxDGKBgFo2AUjAJkAACIUGFcvwuBUwAAAABJRU5ErkJggg==","orcid":"","institution":"University of Rajshahi","correspondingAuthor":true,"prefix":"","firstName":"Chow.","middleName":"M. Sarwar","lastName":"Jahan","suffix":""}],"badges":[],"createdAt":"2024-04-04 16:02:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4218891/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4218891/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54577680,"identity":"66194d80-8667-4a66-9f78-888d536e7472","added_by":"auto","created_at":"2024-04-12 13:58:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":237676,"visible":true,"origin":"","legend":"\u003cp\u003eStudy area of five cities under two climatic zones for the analysis of Heatwaves.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4218891/v1/8e574052a5dd430e0510b1a7.png"},{"id":54576669,"identity":"c0c46175-b5eb-42cc-8f8f-effce95ca17b","added_by":"auto","created_at":"2024-04-12 13:50:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":102754,"visible":true,"origin":"","legend":"\u003cp\u003eTime series of the daily T\u003csub\u003eMAX\u003c/sub\u003e observed from BMD over five cities for the historical period (1996–2022).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4218891/v1/92dea1efd9830253b01c3685.png"},{"id":54576673,"identity":"58f915ee-88b3-4ba8-9906-0fd98b24e76c","added_by":"auto","created_at":"2024-04-12 13:50:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":265862,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a).\u003c/strong\u003e Numberof mild heatwave days (1996–2022) according to BMD criterion across five cities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(b).\u003c/strong\u003e Numberof moderate heatwave days (1996–2022) according to BMD criterion across five cities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(c).\u003c/strong\u003e Number of severe heatwave days (1996–2022) according to BMD criterion across five cities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(d).\u003c/strong\u003e Numberof extreme heatwave days (1996–2022) according to BMD criterion across five cities.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4218891/v1/2676ea6453a5369fdddc33b6.png"},{"id":54576670,"identity":"faff813b-af9c-4763-b9f6-ae0ab2691d1e","added_by":"auto","created_at":"2024-04-12 13:50:55","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":258226,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a).\u003c/strong\u003e Number of heatwaves in Rajshahi (2024-2050) based on CMIP6 temperature projections using the BMD criterion under SSP_245 scenario.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(b)\u003c/strong\u003e Number of heatwaves in Chuadanga (2024-2050) based on CMIP6 temperature projections using the BMD criterion under SSP_245 scenario.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(c).\u003c/strong\u003e Number of heatwaves in Ishwardi (2024-2050) based on CMIP6 temperature projections using the BMD criterion under SSP_245 scenario.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(d).\u003c/strong\u003e Number of heatwaves in Bogra (2024-2050) based on CMIP6 temperature projections using the BMD criterion under SSP_245 scenario.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(e).\u003c/strong\u003e Number of heatwaves in Dinajpur (2024-2050) based on CMIP6 temperature projections using the BMD criterion under SSP_245 scenario.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4218891/v1/bc11097058c26cac1784cf91.png"},{"id":54576672,"identity":"e6992f6e-c9f9-4e3c-a108-30bb5a156502","added_by":"auto","created_at":"2024-04-12 13:50:55","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":256600,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea).\u003c/strong\u003e Number of heatwaves in Rajshahi (2024-2050) based on CMIP6 temperature projections using the BMD criterion under SSP_585 scenario.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb).\u003c/strong\u003e Number of heatwaves in Chuadanga (2024-2050) based on CMIP6 temperature projections using the BMD criterion under SSP_585 scenario.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ec).\u003c/strong\u003e Number of heatwaves in Ishwardi (2024-2050) based on CMIP6 temperature projections using the BMD criterion under SSP_585 scenario.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ed).\u003c/strong\u003e Number of heatwaves in Bogra (2024-2050) based on CMIP6 temperature projections using the BMD criterion under SSP_585 scenario.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ee).\u003c/strong\u003e Number of heatwaves in Dinajpur (2024-2050) based on CMIP6 temperature projections using the BMD criterion under SSP_585 scenario.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4218891/v1/cdab0299f2c9710606b2be8e.png"},{"id":59461967,"identity":"af5981f0-f5a7-4d5f-80d0-3a34885fcf84","added_by":"auto","created_at":"2024-07-02 05:26:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1570228,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4218891/v1/d6189da8-d61e-4658-93b2-3bcbd7a43669.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Characterization of Heatwave Patterns and Its Long-run Predictions Using CMIP6 Model in Western and North-Western Climatic Zones of Bangladesh","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAccording to the\u0026nbsp;UN International Strategy for Disaster Reduction\u0026nbsp;(UNISDR\u0026nbsp;et al. 2015), heatwave is the ten most deadly natural disasters in 2015, where South Asia stands in third in terms of death, and becoming one of the deadliest natural catastrophes with mortality rates rising in both industrialized and developing countries. But it is important to remember that heat-related deaths are mostly preventable. Many developed countries have built heat early warning systems in cities, and study has shown that these systems can save lives by improving readiness program (Ebi et al. 2004; McGregor et al. 2015). In\u0026nbsp;Bangladesh, a country that experiences natural disasters as recurrent phenomenon, and among other the frequency and severity heatwaves as environmental hazards is expected to increase in coming years. Numerous yearly disasters such as tropical cyclones, storm surges, coastal flooding, river erosion, floods, and droughts, cause major losses in terms of human life, property, and land in the country (Uddin and Suravi, 2019).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBangladesh - a densely populated small area country has effected by regional variations in the waves of natural disasters and climate change (Al-Maruf et al., 2020). Although the country is extremely vulnerable to climate change phenomenon (Kreft et al., 2016), but it has lack of information regarding the projections of long-term heatwave patterns that will be expected to rise above current levels (Ripon and Al-Mamun, 2020). According to earlier studies, the temperature has increased by 0.50 °C with a rise in temperature of 0.30 °C during the period of last 20 years in the country (Ripon and Al-Mamun, 2020). The country is situated in the subtropical monsoon belt with diverse seasonal patterns and is divided into seven unique zones - \u0026nbsp;the south-southeast (A), north-eastern (B), northern (C), north-western (D), western (E), south-western (F), and south-central (G) based on overall climate pattern (Rashid 1991). The northwestern (D) zone including Rajshahi, Chuadanga, Ishwardi, Bogra, and Dinajpur, and the western (E) zone in Rajshahi, about 1157 km\u003csup\u003e2\u003c/sup\u003e area have taken for present study \u003cstrong\u003e(Figure 1).\u0026nbsp;\u003c/strong\u003eThe study area is bordered by the Indian states of West Bengal with average maximum temperature is 37.1°C in the summer (March to June), and minimum temperature is 11.2°C in the winter (December to February) with average annual rainfall amount is 1467 mm. According to the Bangladesh Meteorology Department (BMD), Ishwardi has recorded as the hottest temperature (43 \u003csup\u003e0\u003c/sup\u003eC) in the country in 2023.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMoreover, the climatic zones D and E in the north-western part of country are worst victim of the natural calamities such as thunderstorms, floods, cold and heat waves, drought, riverbank erosion, water scarcity etc. (Karmakar, 2019). In recent years, these climatic zones have also become an ‘Urban Heat Island - UHI’ and the problem getting worse every year (Tanvir et al., 2019; Ripon and Al-Mamun, 2020) than other parts of the country because more vulnerable due to heat waves, high temperatures and low humidity. Moreover, the investigation on severe temperature patterns can yield important information about heatwave patterns in the western (E) and northwest (D) climatic zones of the country.\u003c/p\u003e\n\u003cp\u003eGenerally, heatwave predictions are made using General Circulation Models (GCMs), where global temperature changes under various climate change conditions. Nevertheless, further statistical methods are required to improve regional projection accuracy, particularly for extreme events (Nandi, S. and Swain, 2023). According to earlier research, northwest and west-central regions in Bangladesh are expected to face more increases in temperature, with future increases expected to exceed 5 °C by the end of the 21\u003csup\u003est\u003c/sup\u003e century under RCP_85 (Representative Concentration Pathway 85) compared to the reference climate (1981–2005). The average annual mean maximum temperature in the country is predicted to rise by 1.9 °C and 4.0 °C by the end of present century under the RCP_45 and RCP_85 situations, respectively. Similarly, by the end of this century, it is predicted that the annual mean lowest temperature averaged in the country will rise by 2.1 °C and 4.0 °C, respectively, under RCP_45 and RCP_85 situations. Under all emission conditions, the average annual mean temperatures in the country likewise exhibit a significant increase in comparison to the reference climate (Bosu et al 2021). It is predicted that heatwaves will grow more intense and long-run in many parts of the world (Cowan et al. 2014). It is noteworthy that, RCP_45 and RCP_85 among other RCP situations are more frequently cited in the scholarly literature (Nandi and Swain, 2023).\u003c/p\u003e\n\u003cp\u003eTo address the limitations found in CMIP5 (Coupled Model Intercomparison Project Phase 5) model and to suit the changing needs of the climate research community, a new version of the CMIP6 model has recently introduced (Stouffer et al. 2017). The model responses has hampered by CMIP5 with severe technical flaws including difficulties in minimizing systemic biases, weighing the advantages and disadvantages of various mitigation techniques, and precisely measuring radiative forcing. At present CMIP6 model has adopted to overcome these limitation by using the Shared Socioeconomic Pathway (SSP). According to Eyring et al. (2016), these situations prioritize resolving model biases and offer a more coordinated depiction of land surface processes and atmospheric aerosols. They also offer solutions for predicting variability on timeframes more than year. The yearly mean temperature in South Asia is predicted to rise by 2.1 °C and 4.3 °C, respectively, under the model of the SSP_245 and SSP_585 situation (Das and Umamahesh 2022). It is noteworthy to emphasize that these SSP situations are consistent with RCP_45 and RCP_85 predictions under the CMIP5 experiment. Moreover, few studies have been carried out with CMIP6 to predict future changes in heatwave frequency globally (Plecha and Soares 2020; Kim et al. 2020), and also in Bangladesh to date considering data prioritizing meteorological circumstances. Additionally, it is imperative to evaluate their ability to produce accurate meteorological projections in future risk assessments.\u003c/p\u003e\n\u003cp\u003eThe Coupled Model Inter-comparison Project Phase 6 (CMIP6) is a collaborative global initiative focusing on climate phenomenon, which incorporates advanced climate models and standardized experiments to enhance past, present, and future climate changing and understanding. The exploration of future scenarios has performed by the SSPs software tool in five labelled scenarios. Here the SSP_119 and SSP_126 represent the low-emission trajectories with stringent climate policies, aiming to limit global warming to 1.5 °C and below 2 °C. The SSP_245 reveals an intermediate emissions situation with moderate climate policies, and the SSP_370 indicates medium to high-emission situation with limited policies that projects substantial global warming. The SSP_585 represents a high-emission situation with no or limited policies, resulting in the highest radiative forcing with significant global warming. However, SSPs provide a framework for exploring diverse future scenario assessing potential climate impacts; crucial for informing climate science assessments; and understanding the range of possibilities and uncertainties associated with different socio-economic pathways\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eIn present study, SSP245 and SSP585 scenarios referred to as ‘SSP_245’ and ‘SSP_585’ have used to characterize medium and high emission situations respectively. It's crucial to remember that the highest temperatures happen during the day, that of the lowest at night. But the main approach to identify heatwaves is to consider at the maximum daily temperature (\u003cem\u003eT\u003csub\u003eMAX\u003c/sub\u003e\u003c/em\u003e), as the goal of present study is to measure the number of heatwave days to make the results easier for interpretation. The criterion of BMD is given in \u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe heat waves have been occurring more frequently in the recent past (Das and Umamahesh 2022; Patel et al. 2022), and also been considering as personal experiences, physiological alterations, social and political occurrences etc. The extended periods of unusually high temperatures as pointed out by meteorologists and climatologists that results the growing rate of morbidity and mortality rates, and has considered emergencies by legislators. The heat waves happens during the pre-monsoon season when the highest temperature reaches to 36 °C. Depending on the severity and length of the heatwave duration, the BMD has classified as mild heat waves (36–38\u003csup\u003e0\u003c/sup\u003eC), moderate heat waves (38–40\u003csup\u003e0\u003c/sup\u003eC), severe heat waves (40–42°C), and very severe or extreme heat waves (\u0026gt;42°C).\u003c/p\u003e\n\u003cp\u003eKeeping all these concerns in mind, attempt has made in the present study to examine the patterns of heatwaves in the several cities in the western climatic zone (E), and north-western climatic zones (D) in the western part of Bangladesh, with an aims to utilize an ensemble technique to combine four GCM models to improve the accuracy of the results; characterize heatwaves using approaches based on BMD heatwave warnings and evaluating their historical frequency using observed data; and forecasting and analyzing future heatwave characteristics using four CMIP6 model of GCMs and comparing these projections with recorded conditions.\u003c/p\u003e"},{"header":"Datasets and Methodology","content":"\u003cp\u003eA gridded observational dataset covering maximum temperature (\u003cem\u003eT\u003csub\u003eMAX\u003c/sub\u003e\u003c/em\u003e) with a daily temporal resolution and a geographical resolution of 1°×1° has been provided by the BMD during the period of 1981-2022. The BMD carefully measured ground-based observations from the stations spread across the study area to create this gridded dataset, which is subjected to stringent quality assurance processes. In the context of present study, the gridded \u003cem\u003eT\u003csub\u003eMAX\u003c/sub\u003e\u003c/em\u003e dataset of BMD has taken into consideration during the period of 1996-2022 due to its alignment with the historical data derived from Global Climate Models (GCMs). The details of these GCMs models, including their spatial resolutions, and affiliated institutions have given in \u003cstrong\u003eTable 1.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo study present heatwave events, four GCM models that best fit as base for the climate of Bangladesh (Kamruzzaman et al., 2021) have selected, and SSP_245 and SSP_585 models are used as two emission situations. The Earth System Grid Data Portal (https://esgf-node.llnl.gov/search/cmip6/) has provided simulated data from four GCMs in the CMIP6 dataset of two SSPs. The long run time period (1996-2022) has taken for present study. \u0026nbsp;The historical \u003cem\u003eTmax\u003c/em\u003e in model has used to extract the historical \u003cem\u003eTmax\u003c/em\u003e for the years 1996–2014, and the projected \u003cem\u003eTmax\u003c/em\u003e for SSP_245 and SSP_585 have used to obtain the historical \u003cem\u003eTmax\u003c/em\u003e for the period of 2015–2022. Consequently, two sets of historical \u003cem\u003eTmax\u003c/em\u003e are obtained. There exists differences between the historical data from the model and the observed data from the BMD, which are described to load factors, parameterization, resolution, and climate variability. Here the additive scaling method has used to address these biases. This statistical technique makes it easier for historical outcomes to match model-simulated results, and ensures a smooth transition into projected values for the future (Ullah, S. et al., 2022). An additive scale between BMD observed and simulated \u003cem\u003eTmax\u003c/em\u003e during the time frame of 1996–2022 has calculated for each daily maximum temperature of model as follows:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eT\u003csub\u003eSIMULATED\u003c/sub\u003e\u003c/em\u003e - \u003cem\u003eT\u003csub\u003eOBSERVED\u003c/sub\u003e\u003c/em\u003e = \u003cem\u003eT\u003csub\u003eADDITIVE_SCALING\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eMoreover, the future estimation of \u003cem\u003eTmax\u003c/em\u003e in model during period of 2024–2050 has updated to include \u003cem\u003eT\u003csub\u003eADDITIVE_SCALING\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eT\u003csub\u003eADDITIVE_SCALING\u003c/sub\u003e\u003c/em\u003e + \u003cem\u003eT\u003csub\u003eMAX_MODEL_PROJECTED\u003c/sub\u003e\u003c/em\u003e = \u003cem\u003eT\u003csub\u003eMAX_PROJECTED\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe future projected \u003cem\u003eTmax\u003c/em\u003e for four models have obtained after correcting of biases. The four models are then combined using an ensemble technique, which can produce results that are more accurate than those obtained from using a single model (Riccio, 2007). Since the median is less sensitive to extreme events, so it has chosen in ensemble strategy of the present study.\u0026nbsp;\u003c/p\u003e\u003cp\u003eA gridded observational dataset covering maximum temperature (\u003cem\u003eT\u003csub\u003eMAX\u003c/sub\u003e\u003c/em\u003e) with a daily temporal resolution and a geographical resolution of 1°×1° has been provided by the BMD during the period of 1981-2022. The BMD carefully measured ground-based observations from the stations spread across the study area to create this gridded dataset, which is subjected to stringent quality assurance processes. In the context of present study, the gridded \u003cem\u003eT\u003csub\u003eMAX\u003c/sub\u003e\u003c/em\u003e dataset of BMD has taken into consideration during the period of 1996-2022 due to its alignment with the historical data derived from Global Climate Models (GCMs). The details of these GCMs models, including their spatial resolutions, and affiliated institutions have given in \u003cstrong\u003eTable 1.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo study present heatwave events, four GCM models that best fit as base for the climate of Bangladesh (Kamruzzaman et al., 2021) have selected, and SSP_245 and SSP_585 models are used as two emission situations. The Earth System Grid Data Portal (https://esgf-node.llnl.gov/search/cmip6/) has provided simulated data from four GCMs in the CMIP6 dataset of two SSPs. The long run time period (1996-2022) has taken for present study. \u0026nbsp;The historical \u003cem\u003eTmax\u003c/em\u003e in model has used to extract the historical \u003cem\u003eTmax\u003c/em\u003e for the years 1996–2014, and the projected \u003cem\u003eTmax\u003c/em\u003e for SSP_245 and SSP_585 have used to obtain the historical \u003cem\u003eTmax\u003c/em\u003e for the period of 2015–2022. Consequently, two sets of historical \u003cem\u003eTmax\u003c/em\u003e are obtained. There exists differences between the historical data from the model and the observed data from the BMD, which are described to load factors, parameterization, resolution, and climate variability. Here the additive scaling method has used to address these biases. This statistical technique makes it easier for historical outcomes to match model-simulated results, and ensures a smooth transition into projected values for the future (Ullah, S. et al., 2022). An additive scale between BMD observed and simulated \u003cem\u003eTmax\u003c/em\u003e during the time frame of 1996–2022 has calculated for each daily maximum temperature of model as follows:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eT\u003csub\u003eSIMULATED\u003c/sub\u003e\u003c/em\u003e - \u003cem\u003eT\u003csub\u003eOBSERVED\u003c/sub\u003e\u003c/em\u003e = \u003cem\u003eT\u003csub\u003eADDITIVE_SCALING\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eMoreover, the future estimation of \u003cem\u003eTmax\u003c/em\u003e in model during period of 2024–2050 has updated to include \u003cem\u003eT\u003csub\u003eADDITIVE_SCALING\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eT\u003csub\u003eADDITIVE_SCALING\u003c/sub\u003e\u003c/em\u003e + \u003cem\u003eT\u003csub\u003eMAX_MODEL_PROJECTED\u003c/sub\u003e\u003c/em\u003e = \u003cem\u003eT\u003csub\u003eMAX_PROJECTED\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe future projected \u003cem\u003eTmax\u003c/em\u003e for four models have obtained after correcting of biases. The four models are then combined using an ensemble technique, which can produce results that are more accurate than those obtained from using a single model (Riccio, 2007). Since the median is less sensitive to extreme events, so it has chosen in ensemble strategy of the present study.\u0026nbsp;\u003c/p\u003e"},{"header":"Results and Discussions","content":"\u003cp\u003eFor the observed historical 27 years (1996-2022) data \u0026nbsp;of daily maximum temperatures (\u003cem\u003eT\u003csub\u003eMAX\u003c/sub\u003e\u003c/em\u003e) for each of the five cities \u003cstrong\u003e(Figure 2)\u003c/strong\u003e, it is revealed that \u0026nbsp; Dinajpur and Bogra vary from 23 °C to 24 °C in the winter to 35 °C to 37 °C in the summer having pleasant weather conditions. On the other hand, that of T\u003csub\u003eMAX\u003c/sub\u003e varies from 22 °C – 23 °C during winter days to 35 °C – 41 °C during summer days in Rajshahi, Chuadanga, and Ishwardi representing unusually hightemperature, especially in the summer months due to areal strong solar radiation, high evaporation, and limited rainfall.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBased on the BMD criterion, the number of annual heatwave days for the historical period in the five cities are presented in \u003cstrong\u003eFigure 3(a-d).\u003c/strong\u003e Accordingly, heat wave phenomenon in the study area has divided into categories as mild, moderate, severe, and extreme. It is observed that historically the area experiences a number of moderate heatwaves days with rising trend especially after 2010. Among the studied cities, Rajshahi stands with the highest number of moderate heatwave days, notably in recent decade (2010-2022) \u003cstrong\u003e(Figure 3a)\u0026nbsp;\u003c/strong\u003erepresenting\u0026nbsp;typically 30 and 50 days every year along with nearly largest (75 days) in2022. Similarly, Chuadanga and Ishwardi demonstrate essentially identical trends \u003cstrong\u003e(Figure 3a),\u003c/strong\u003e with an average number of 20 to 40 mild heatwave days each year. Although there has been a rising trend of heatwave days in the last few years in Bogra and Dinajpur, but recent data shows that comparatively less number of mild heat waves where typically annual counts range from 10-20 days \u003cstrong\u003e(Figure 3a).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn present study area, moderate heatwave days occur annually where Rajshahi has the highest frequency and occurs in 5-20 days with exceptionally 25 days in 2012; however, in subsequent years, this number has decreased \u003cstrong\u003e(Figure 3b).\u003c/strong\u003e Similarly Chuadanga and Ishwardi also have 5-15 mild heatwaves days annually, though their frequency has decreased in recent time \u003cstrong\u003e(Figure 3b).\u003c/strong\u003e On the other hand, Bogra and Dinajpur normally have 0-5 days with moderate heatwaves days with exceptional rising trend in days in 2014 of nearly 8 days \u003cstrong\u003e(Figure 3b),\u0026nbsp;\u003c/strong\u003eandthe frequency of mild heatwaves has declined in the last few years.\u003c/p\u003e\n\u003cp\u003eIt is visible that a discernible rise in the frequency of severe heatwave days has observed in Chuadanga, Rajshahi, and Ishwardi over time in recent decade especially after 2008, whereas Chuadanga and Rajshahi have the highest number of severe heatwave days in 2014 which has followed in subsequent decrease in frequency significantly\u003cstrong\u003e\u0026nbsp;(Figure 3c).\u0026nbsp;\u003c/strong\u003eThere have fluctuation with moderately frequent severe heatwave days in Ishwardi with duration of 1-5 days. In contrast, Bogra and Dinajpur have witnessed few cases of severe heatwave days with no report of severe heatwave days after 2013 \u003cstrong\u003e(Figure 3c).\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe long-run historical annual occurrences of heatwave days in the cities of the study area such as Rajshahi, Chuadanga, and Ishwardi have experienced the most extremities \u003cstrong\u003e(Figure 3d)\u003c/strong\u003e. After some specific time period, no city has experienced extreme heatwaves days except Chuadanga which encounters single day of extreme heatwaves in both 2005 and 2014 \u003cstrong\u003e(Figure 3d)\u003c/strong\u003e. Apart from these isolated occurrences, no other cities has observed extreme heatwaves days in the past couples of years. In the study area, five cities have shown a rise in the overall frequency of heat wave days over time, particularly after 2010. The cities such as Bogra and Dinajpur are comparatively less prone to heatwaves days than that of Rajshahi, Chuadanga, and Ishwardi.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe assessment of upcoming heat waves is essential, and the dependability of dataset is critical. The variety of GCM-based simulations from the CMIP6 study has used in present study, and it is crucial to contrast their \u003cem\u003eT\u003csub\u003eMAX\u003c/sub\u003e\u003c/em\u003e projections with the observed data. Consequently, using BMD data, the chosen models (ACCESS-CM2, ACCESS-ESM1-5, CanESM5 and INM-CM5-0) are assessed for \u003cem\u003eT\u003csub\u003eMAX\u003c/sub\u003e\u003c/em\u003e forecast across the long run historical data. To improve accuracy, differences between the model historical data and observed BMD data are found and fixed. The four models are then combined using an ensemble technique to yield findings that are more accurate as if only one model will be used.\u003c/p\u003e\n\u003cp\u003eTo be consistent with the previous 27-year timeframe, the anticipated period has considered as 2024-2050. The number of heatwave days annually, based on BMD criteria for SSP_245 and SSP_585 situations from CMIP6 temperature projections in five cities in the study area during this period is illustrated in \u003cstrong\u003eFigure 4(a-e)\u0026nbsp;\u003c/strong\u003eand \u003cstrong\u003eFigure 5(a-e)\u003c/strong\u003e respectively, and the total number of \u0026nbsp;heatwave days are summarized in \u003cstrong\u003eTable 2\u003c/strong\u003e. It is obvious from \u003cstrong\u003eFigure 4(a-e)\u0026nbsp;\u003c/strong\u003eand \u003cstrong\u003eFigure 5(a-e)\u003c/strong\u003e that these cities will experience a significantly different number of heatwave days in the future than they did during the historical period. The SSP_245 situation predicts that during the period of 2024-2050, there will be number of heat days such as 614, 597, 526, 340, and 325 of mild nature in Rajshahi, Chuadanga, Ishwardi, Bogra, and Dinajpur, respectively. It is predicted that there will be moderate heatwaves over these cities for 225, 217, 171, 57, and 69 days, respectively. The forecasted dates for severe heatwaves are 85, 75, 53, 10, and 5 days respectively, and that of extreme heatwaves are 31, 26, 15, 1, and 0 days respectively. Similar to this, the SSP_585 situation projects that during the years 2024–2050, there will be 763, 665, 637, 405, 412 days of mild heatwaves over Rajshahi, Chuadanga, Ishwardi, Bogra, and Dinajpur, and 289, 289, 222, 111, and 95 days of moderate heatwaves. Projected durations of severe heatwaves are 121, 75, 69, 18, and 14 days, respectively. There will be extreme heat waves of 43, 36, 28, 2, and 2 days, respectively. The highest anticipated number of mild, moderate, severe, and extreme heatwave days in a year for Rajshahi under the SSP_245 situations 47, 21, 13, and 9 days, respectively during the period of 2023–2050. Chuadanga has 32, 19, 15, and 11 days, in that order; and 41, 17, 8, and 5 days, respectively for Ishwardi. Bogra has 24, 10, 3, and 1 days, in that order, and 38, 10, 2, and 0 days, respectively, for Dinajpur. In similar manner, the highest anticipated number of mild, moderate, severe, and extreme heatwave days in a year in Rajshahi for the SSP_585 situations is 63, 42, 29, and 12 days, respectively. Chuadanga has 55, 34, 13, and 7 days in that order. 52, 42, 16, and 10 days, respectively, for Ishwardi. 31, 13, 4, and 1 day for Bogra in that order. It is 40, 14, 3, and 1 day for Dinajpur respectively. \u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003erepresents a comparison of predicted heatwave days for the future with historical data.\u003c/p\u003e\n\u003cp\u003eThe results of this study highlight the rising risks that heatwaves pose in the future due to climate change effect, especially in Rajshahi, Chuadanga, and Ishwardi. \u003cstrong\u003eTable 2\u003c/strong\u003e shows that while severe and strong heatwaves are predicted to grow significantly in all five cities, and mild and moderate heatwave days are predicted to decline. This means that there will be more heatwave days in the future time, which highlights the way of urgently strong legislation has to be formulated to combat the growing effects of heatwaves days in the present studied cities. Heatwave days are associated with serious health disorders like diabetes mellitus morbidity (Song et al. 2021), schizophrenia (Tang et al. 2021), infectious diarrhea (Liang et al. 2021), and mental disorders (Yoo et al. 2021). In addition, heatwaves days pose a significant risk to public health due to rising ambient temperatures and extreme conditions. Furthermore, Ran et al. (2020) has shown that there is a higher chance of flash droughts in areas where heatwaves days occur frequently. (Singh et al. 2021; Swain et al. 2022a). Recent studies have repeatedly shown that extreme weather is becoming more frequent and severe due to climate change, especially in metropolitan areas. Thus, it is essential to take preventative actions to learn the negative impact of intense heatwaves days.\u003c/p\u003e\n\u003cp\u003eThe United Nations General Assembly has developed 17 Sustainable Development Goals (SDGs) in 2015 (UN DESA 2016). More sustainable future, three specific of SDGs in line with current study are: \u0026nbsp;SDG 3 (ensuring healthy lives and promoting well-being for all at all ages); SDG 11 (making cities inclusive, safe, resilient, and sustainable); and SDG 13 (taking urgent action to combat climate change and its impacts) (Swain et al. 2022b, 2022d). \u0026nbsp;The enhancement of cities' resilience and adaptability to climate hazards and disasters, especially in developing countries, is the unifying target of these SDGs goals (Guptha et al. 2021, 2022). The study urgently highlight the need for planning of heatwave preparedness. In this context, proactive interventions should include planting trees, implementing smart growth strategies, installing green roofs, creating urban inland water bodies, and using materials with high albedo on the outside of buildings (Patel et al. 2022b). Additionally, the SSP5-8.5 situation considers much higher number of heatwave days than that of SSP2-4.5 in every city in the study area, and strongly demands strong climate policies to be developed, especially for urban centers in the western part of Bangladesh.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe frequency of extreme weather events like heatwave is becoming a growing concern as a climate change effect. Present study aimed to assess the features of heatwaves in five cities such as Rajshahi, Chuadanga, Ishwardi, Bogra, and Dinajpur in the northwestern part of country that are particularly sensitive to heatwave days. The study has included forecasts of heatwaves for the period of from 2024-2050 considering the long run historical data from 1996-2022. The heatwave alerts of BMD have used to identify heatwaves. Four GCMs from CMIP6 have examined for bias correction in their maximum daily temperature (\u003cem\u003eT\u003csub\u003eMAX\u003c/sub\u003e\u003c/em\u003e) outputs to evaluate temperature outputs. The four models are then combined using an ensemble technique to improve accuracy. This method has made it easier to create temperature predictions for the future that correspond to socioeconomic pathways (SSP_245 and SSP_585). Finally it may reveal that:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eIn comparison to the historical (1996-2022) \u003cem\u003eT\u003csub\u003eMAX\u003c/sub\u003e\u003c/em\u003e, Bogra and Dinajpur shows comfortable weather with fewer days of heatwaves, meeting the standards set by the. On the other hand, out of the five cities Rajshahi, Chuadanga, and Ishwardi have the most heatwave days.\u003c/li\u003e\n \u003cli\u003eThe BMD heatwave criteria are applied for historical as well as predicted \u003cem\u003eTmax\u003c/em\u003e of GCMs model data. To improve the accuracy of the results, four models from the CMIP6 experiment have taken, subjected to bias correction, and then combined using an ensemble technique.\u003c/li\u003e\n \u003cli\u003eThe anticipated future shows a notable increase in the number of heatwave days. Out of the five cities, Rajshahi, Chuadanga, and Ishwardi are expected to be the most severely affected according to the CMIP6 model that is being used.\u003c/li\u003e\n \u003cli\u003eIn comparison to SSP 2–4.5, the frequency and intensity of heatwave days will be more noticeable in all cities under SSP 5–8.5, and demonstrates the human activity directly contributes to the rise in heatwaves.\u003c/li\u003e\n \u003cli\u003eThe results highlight the urgency of taking immediate action to improve heatwave readiness. These study results are expected to enhance the accomplishment of sustainable development goals (SDG 3, SDG 11, and SDG 13) by guiding proactive steps to mitigate the consequences of severe heatwave days.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthor 1 - Rayhan Ahmad: Conceptualization, Formal analysis, Investigation, Data management, software, writing-original draft.Author 2 - Md. Khalid Hasan: Methodology, software, Visualization, investigation.Corresponding Author - Chow. M. Sarwar Jahan: Review and editing. All authors reviewed the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAl-Maruf, A., Jenkins, C., Islam, A., Sarmin, S., 2020. Coastal zone of Bangladesh: a tale of Pessimism and Optimism. In: Climate Adaptation for a Sustainable Economy: Lessons from Bangladesh, an Emerging Tiger of Asia. Asian Political, Economic and Social Issues. Nova Science Publishers.\u003c/li\u003e\n \u003cli\u003eBoni, Z., Bieńkowska, Z., Chwałczyk, F. et al. What is a heat(wave)? An interdisciplinary perspective. Climatic Change 176, 129 (2023). https://doi.org/10.1007/s10584-023-03592-3\u003c/li\u003e\n \u003cli\u003eBosu, H., Rashid, T., Mannan, A., and Meandad, J. (2021). Climate change analysis for Bangladesh using CMIP5 models. Dhaka Univ. J. Earth Environ. Sci. 9, 1\u0026ndash;12. doi:10.3329/ dujees.v9i1.54856\u003c/li\u003e\n \u003cli\u003eCowan T, Purich A, Perkins S, Pezza A, Boschat G, Sadler K (2014) More frequent, longer, and hotter heat waves for Australia in the twenty-first century. J Clim 27(15):5851\u0026ndash;5871\u003c/li\u003e\n \u003cli\u003eDas J, Umamahesh NV (2022) Heatwave magnitude over India under changing climate: projections from CMIP5 and CMIP6 experiments. Int J Climatol 42(1):331\u0026ndash;351\u003c/li\u003e\n \u003cli\u003eKarmakar, S., 2019. Pattern of climate change and its impacts in northwestern Bangladesh 2019. J. Eng. Sci. 10 (2), 33\u0026ndash;48.\u003c/li\u003e\n \u003cli\u003eKim MK, Yu DG, Oh JS, Byun YH, Boo KO, Chung IU, Park JS, Park DSR, Min SK, Sung HM (2020) Performance evaluation of CMIP5 and CMIP6 models on heatwaves in Korea and associated teleconnection patterns. J Geophys Res Atmos 125(23):e2020JD032583\u003c/li\u003e\n \u003cli\u003eKreft, S., Eckstein, D., Melchior, I., 2016. Global Climate Risk Index 2017. Germanwatch, Germany.\u003c/li\u003e\n \u003cli\u003eMcGregor, G. R., P. Bessemoulin, K. Ebi, and B. Menne, 2015: Heat- waves and health: Guidance on warning- \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; system development. WMO Rep. 1142, 114 pp., http://www.who.int/globalchange/ \u0026nbsp; \u0026nbsp; publications/WMO_WHO_Heat_Health_Guidance_2015.pdf.\u003c/li\u003e\n \u003cli\u003eNandi, S., Swain, S. Analysis of heatwave characteristics under climate change over three highly populated cities of South India: a CMIP6-based assessment. Environ Sci Pollut Res 30, 99013\u0026ndash;99025 (2023).\u0026nbsp;\u003ca href=\"https://doi.org/10.1007/s11356-022-22398-x\"\u003ehttps://doi.org/10.1007/s11356-022-22398-x\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003ePatel P, Jamshidi S, Nadimpalli R, Aliaga DG, Mills G, Chen F, Demuzere M, Niyogi D (2022) Modeling large-scale heatwave by incorporating enhanced urban representation. J Geophys Res Atmos 127(2):e2021JD035316\u003c/li\u003e\n \u003cli\u003ePatel P, Thakur PK, Aggarwal SP, Garg V, Dhote PR, Nikam BR, Swain S, Al-Ansari N (2022b) Revisiting 2013 Uttarakhand flash floods through hydrological evaluation of precipitation data sources and morphometric prioritization. Geomat Nat Haz Risk 13(1):646\u0026ndash;666\u003c/li\u003e\n \u003cli\u003ePlecha SM, Soares PM (2020) Global marine heatwave events using the new CMIP6 multi-model ensemble: from shortcomings in present climate to future projections. Environ Res Lett 15(12):124058\u003c/li\u003e\n \u003cli\u003eRipon, H., Al-Mamun, S., 2020. Climate change and its divers impact on the rural infrastructures in Bangladesh. J. Disaster Adv. 13 (9).\u003c/li\u003e\n \u003cli\u003eTanvir, Hossain, Humayain, Kabir Md., Mahmud, Khan Mohammed Bayezid, 2019. Flood vulnerabilities, impacts and their coping techniques in Island Areas of Muladi Upazila in Barishal District, Bangladesh. Disaster Adv. 12 (8), 41\u0026ndash;59.\u003c/li\u003e\n \u003cli\u003eUddin, M.S., Suravi, R.H., 2019. The rise of a new disaster in Bangladesh: Analysis of characteristics and vulnerabilities of lightning during March to September 2018. In: Proceedings on International Conference on Disaster Risk Management, Dhaka, Bangladesh, January 12-14.\u003c/li\u003e\n \u003cli\u003eUN DESA (2016) The Sustainable Development Goals Report 2016. United Nations Publications, United Nations Department of Economic and Social Affairs. https://doi.org/10.18356/3405d09fen\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003eGCMs from CMIP6 used in this study.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"623\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.615384615384615%\"\u003e\n \u003cp\u003eSl. No.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.23076923076923%\"\u003e\n \u003cp\u003eModels\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.25%\"\u003e\n \u003cp\u003eInstitution \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.903846153846153%\"\u003e\n \u003cp\u003eResolution\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.615384615384615%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.23076923076923%\"\u003e\n \u003cp\u003eACCESS-CM2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.25%\"\u003e\n \u003cp\u003eAustralian Community Climate and Earth-System Simulator \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.903846153846153%\"\u003e\n \u003cp\u003e1.88\u0026deg; \u0026times; 1.25\u0026deg;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.615384615384615%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.23076923076923%\"\u003e\n \u003cp\u003eACCESS-ESM1-5 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.25%\"\u003e\n \u003cp\u003eAustralian Community Climate and Earth-System Simulator \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.903846153846153%\"\u003e\n \u003cp\u003e1.88\u0026deg; \u0026times; 1.25\u0026deg;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.615384615384615%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.23076923076923%\"\u003e\n \u003cp\u003eCanESM5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.25%\"\u003e\n \u003cp\u003eCanadian Earth System Model \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.903846153846153%\"\u003e\n \u003cp\u003e2.8\u0026deg; \u0026times; 2.8\u0026deg;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.615384615384615%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.23076923076923%\"\u003e\n \u003cp\u003eINM-CM5-0 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.25%\"\u003e\n \u003cp\u003eInstitute for Numerical Mathematics, Russia \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.903846153846153%\"\u003e\n \u003cp\u003e2\u0026deg;\u0026times;1. 5\u0026deg;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003eBMD-classified heat waves.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"331\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eHeat Wave\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMaximum Temperature Range\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMild Heat Wave \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e36-38\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eModerate Heatwave \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e38-40\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSevere Heatwave \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e40-42\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eExtreme Heatwave \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026gt;42\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003eTotal number of heatwave days throughout the historical period (1996\u0026ndash;2022) based on BMD data and future situations (SSP245 and SSP585 from 2024 to 2050 year) based on CMIP6 (GCMs data by using BMD criteria for five cities).\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"605\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.958677685950413%\"\u003e\n \u003cp\u003eCity name: Rajshahi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.198347107438018%\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.49586776859504%\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.49586776859504%\"\u003e\n \u003cp\u003eSevere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.85123966942149%\"\u003e\n \u003cp\u003eExtreme\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.958677685950413%\"\u003e\n \u003cp\u003eHistorical Period\u003c/p\u003e\n \u003cp\u003e(1996-2022)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.198347107438018%\"\u003e\n \u003cp\u003e950\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.49586776859504%\"\u003e\n \u003cp\u003e310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.49586776859504%\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.85123966942149%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.958677685950413%\"\u003e\n \u003cp\u003eSSP245 (2024-2050)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.198347107438018%\"\u003e\n \u003cp\u003e614\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.49586776859504%\"\u003e\n \u003cp\u003e225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.49586776859504%\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.85123966942149%\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.958677685950413%\"\u003e\n \u003cp\u003eSSP585(2024-2050)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.198347107438018%\"\u003e\n \u003cp\u003e763\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.49586776859504%\"\u003e\n \u003cp\u003e289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.49586776859504%\"\u003e\n \u003cp\u003e121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.85123966942149%\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.958677685950413%\"\u003e\n \u003cp\u003eCity name: \u0026nbsp;Chuadanga \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.198347107438018%\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.49586776859504%\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.49586776859504%\"\u003e\n \u003cp\u003eSevere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.85123966942149%\"\u003e\n \u003cp\u003eExtreme\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.958677685950413%\"\u003e\n \u003cp\u003eHistorical period \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(1996-2022) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.198347107438018%\"\u003e\n \u003cp\u003e851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.49586776859504%\"\u003e\n \u003cp\u003e247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.49586776859504%\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.85123966942149%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.958677685950413%\"\u003e\n \u003cp\u003eSSP245(2024-2050) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.198347107438018%\"\u003e\n \u003cp\u003e597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.49586776859504%\"\u003e\n \u003cp\u003e217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.49586776859504%\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.85123966942149%\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.958677685950413%\"\u003e\n \u003cp\u003eSSP585(2024-2050) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.198347107438018%\"\u003e\n \u003cp\u003e665\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.49586776859504%\"\u003e\n \u003cp\u003e289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.49586776859504%\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.85123966942149%\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.958677685950413%\"\u003e\n \u003cp\u003eCity name: \u0026nbsp;Ishwardi \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.198347107438018%\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.49586776859504%\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.49586776859504%\"\u003e\n \u003cp\u003eSevere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.85123966942149%\"\u003e\n \u003cp\u003eExtreme\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.958677685950413%\"\u003e\n \u003cp\u003eHistorical period \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(1996-2022) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.198347107438018%\"\u003e\n \u003cp\u003e823\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.49586776859504%\"\u003e\n \u003cp\u003e191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.49586776859504%\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.85123966942149%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.958677685950413%\"\u003e\n \u003cp\u003eSSP245(2024-2050) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.198347107438018%\"\u003e\n \u003cp\u003e526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.49586776859504%\"\u003e\n \u003cp\u003e171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.49586776859504%\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.85123966942149%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.958677685950413%\"\u003e\n \u003cp\u003eSSP585(2024-2050) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.198347107438018%\"\u003e\n \u003cp\u003e637\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.49586776859504%\"\u003e\n \u003cp\u003e222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.49586776859504%\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.85123966942149%\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.958677685950413%\"\u003e\n \u003cp\u003eCity name: \u0026nbsp;Bogra \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.198347107438018%\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.49586776859504%\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.49586776859504%\"\u003e\n \u003cp\u003eSevere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.85123966942149%\"\u003e\n \u003cp\u003eExtreme\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.958677685950413%\"\u003e\n \u003cp\u003eHistorical period\u003c/p\u003e\n \u003cp\u003e(1996-2022) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.198347107438018%\"\u003e\n \u003cp\u003e373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.49586776859504%\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.49586776859504%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.85123966942149%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.958677685950413%\"\u003e\n \u003cp\u003eSSP245(2024-2050) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.198347107438018%\"\u003e\n \u003cp\u003e340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.49586776859504%\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.49586776859504%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.85123966942149%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.958677685950413%\"\u003e\n \u003cp\u003eSSP585(2024-2050) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.198347107438018%\"\u003e\n \u003cp\u003e405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.49586776859504%\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.49586776859504%\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.85123966942149%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.958677685950413%\"\u003e\n \u003cp\u003eCity name: \u0026nbsp;Dinajpur \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.198347107438018%\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.49586776859504%\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.49586776859504%\"\u003e\n \u003cp\u003eSevere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.85123966942149%\"\u003e\n \u003cp\u003eExtreme\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.958677685950413%\"\u003e\n \u003cp\u003eHistorical period\u003c/p\u003e\n \u003cp\u003e(1996-2022) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.198347107438018%\"\u003e\n \u003cp\u003e350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.49586776859504%\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.49586776859504%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.85123966942149%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.958677685950413%\"\u003e\n \u003cp\u003eSSP245(2024-2050) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.198347107438018%\"\u003e\n \u003cp\u003e325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.49586776859504%\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.49586776859504%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.85123966942149%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.958677685950413%\"\u003e\n \u003cp\u003eSSP585(2024-2050) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.198347107438018%\"\u003e\n \u003cp\u003e412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.49586776859504%\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.49586776859504%\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.85123966942149%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Heatwave, Maximum temperature, CMIP6, Northwestern Climatic Zones, Bangladesh","lastPublishedDoi":"10.21203/rs.3.rs-4218891/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4218891/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGlobally, the hazards related to climate change effect such as tropical cyclones, storm surges, coastal flooding, river erosion, floods, droughts, heatwave, cold wave etc. are burning challenge that drastically effects people’s food security, health, ecosystems and society as a whole. In recent decade, rising trend of unusually prolong high temperature or heatwave episode is a burning concern. Present study area address the heatwave patterns in western climatic zone (E), and north-western climatic zone (D) in the north-western part of Bangladesh. Using historical maximum temperature (T\u003csub\u003eMAX\u003c/sub\u003e) data during the period of 1996-2014, and model-projected \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003eMAX\u003c/em\u003e\u003c/sub\u003e data (2015- 2022) have collected. Moreover, two shared socio-economic tools such as SSP_245 and SSP_585 have used in present study along with projected data for the years of 2024–2050. The study uses heatwave alerts from the Bangladesh Meteorological Department (BMD) to classify heatwave days into four categories: mild, moderate, severe, and extreme. To improve accuracy, four general circulation models (GCMs) from the Coupled Model Inter-comparison Project phase 6 (CMIP6) have assessed following bias correction, and merged using an ensemble technique. The median has used to reduce extreme value sensitivity. According to the BMD criteria, the results show that severe and exceptional hot days have occurred often in recent years. This tendency could continue into the future, putting a sizable population at risk. The heatwave days are significantly more common under SSP_585 than they are under SSP_245, demonstrating the straightforward influence of human activity on heatwave regularity. To lessen the negative effects of heatwaves, fair and practical measures for preparing for and responding to them should be developed, and will be facilitated by this thorough analysis of heatwave projections. The results of present study highlight the urgency of taking immediate action to improve heat wave readiness. These study results are expected to enhance the accomplishment of the Sustainable Development Goals (SDG 3, SDG 11, and SDG 13) by guiding proactive steps to mitigate the consequences of severe heat wave days.\u003c/p\u003e","manuscriptTitle":"Characterization of Heatwave Patterns and Its Long-run Predictions Using CMIP6 Model in Western and North-Western Climatic Zones of Bangladesh","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-12 13:50:41","doi":"10.21203/rs.3.rs-4218891/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"418ddb39-c903-4617-ba40-62031942cf30","owner":[],"postedDate":"April 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-02T05:10:33+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-12 13:50:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4218891","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4218891","identity":"rs-4218891","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}