Assessing the Impacts of Future Climate Extremes on Boro Rice Cultivation in the Northeastern Haor Region of Bangladesh: Insights from CMIP6 Multi-Model Ensemble Projections

Assessing the Impacts of Future Climate Extremes on Boro Rice Cultivation in the Northeastern Haor Region of Bangladesh: Insights from CMIP6 Multi-Model Ensemble Projections | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Assessing the Impacts of Future Climate Extremes on Boro Rice Cultivation in the Northeastern Haor Region of Bangladesh: Insights from CMIP6 Multi-Model Ensemble Projections Mohammad Kamruzzaman, HM Touhidul Islam, Md. Sazzadur Rahman, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4007462/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Dec, 2024 Read the published version in Theoretical and Applied Climatology → Version 1 posted 15 You are reading this latest preprint version Abstract Northeastern Bangladesh is highly vulnerable to the intensifying impacts of climate change, with extreme climatic events posing a significant threat to rice production. This study examines the projected changes in five key thermal stress indices and heavy rainfall during the rice reproductive phase using 15 global climate models (GCMs) under moderate (SSP2-4.5) and high (SSP5-8.5) emission scenarios. Statistical downscaling and bias correction techniques were employed to generate daily climate data for rainfall, maximum temperature (Tmax), and minimum temperature (Tmin). The Mann–Kendall (MMK) test was applied to identify future trends in these extreme events. The results reveal a substantial decrease in cold stress indices, with three consecutive cold days (CCD3) and six consecutive cold days (CCD6) projected to decline by approximately 9 days. Notably, heat stress indices are anticipated to increase, with hot days (HD) and consecutive hot days (CHD) rising by 18 and 11, respectively. Heavy rainfall days (HR) did not exhibit significant changes. The projected rise in temperatures above 35°C during the rice reproductive phase, encompassing critical stages such as flowering, gametophyte development, anthesis, and pollination and fertilization, suggests adverse consequences for rice yields. These findings underscore the urgency of implementing specific adaptation and mitigation measures to minimize potential yield losses in a future characterized by elevated temperatures. Such measures may include cultivating heat-tolerant rice varieties, adjusting planting windows, and diversifying rice varieties with varying growth durations. Extreme climate events climate change Flash floods future projections heat-tolerant varieties growth duration yield losses Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction The average temperature of the Earth's surface has risen by 1.09°C (0.95–1.20°C) from 2011 to 2020 compared to the period from 1850–1900 (IPCC 2023 ). This temperature rise has resulted in a notable increase in the frequency and severity of extreme climatic occurrences such as floods, droughts, and heatwaves (IPCC 2014 ; Bai et al. 2021 ; Islam et al. 2021 ). Climate change (CC) has significantly impacted phenology, agricultural yields, water utilization, and public health (He et al. 2018 ; Chandarak et al. 2023 ). Each 1°C rise in the mean temperature throughout the rice farming season resulted in a 6.2% decrease in the rice yield (Lyman et al. 2013 ). Therefore, comprehending and projecting the spatiotemporal alterations in extreme climatic conditions is of utmost importance for establishing adaptation plans to mitigate climate-related risks. Rice is a vital staple food for people worldwide, nourishing over 50% of the world's population (He et al. 2018 ). This essential crop not only sustains a primary source of income for farmers across Asia and South America and progressively in Africa but also plays a pivotal role in ensuring food security worldwide (He et al. 2018 ; Kumar et al. 2023 ; Chandarak et al. 2023 ). Bangladesh is positioned as the fourth largest country globally in terms of both rice land area and yields (Uddin and Dhar 2018 ). Rice cultivation primarily occurs in subtropical climates, making it susceptible to frequent exposure to extremely high temperatures. Thermal stress can significantly decrease crop productivity, especially during the flowering or reproductive stages (Luo 2011 ; Gourdji et al. 2013 ). In the reproductive stage, temperatures over 30°C might impact panicle differentiation (PD). In contrast, temperatures ranging from 33 to 35°C during anthesis can lead to the sterility of florets (Matsui and Hasegawa 2019 ). On the other hand, Satake ( 1976 ) reported that cold-sensitive varieties experience cold damage at the reproductive stage when the air temperature drops below 20°C, whereas cold-tolerant varieties are affected at 15°C. Crop damage, particularly in terms of spikelet sterility, occurs if the air temperature remains below the critical low temperature for three consecutive days during the reproductive stage. However, the severity of the damage increases significantly if the low temperature persists for more than 5–6 days (Rashid and Yasmeen 2018 ). As temperatures continue to rise in future Bangladesh (Islam et al. 2023 ; Kamruzzaman et al. 2023c ), heat stress has emerged as a foremost concern for rice yield (Das et al. 2014 ; IPCC 2014 ; Sánchez et al. 2014 ; Chaturvedi et al. 2021 ). Global climate models (GCMs) serve as the primary technique for comprehending the possible consequences of CC. The Coupled Model Intercomparison Project phase 6 (CMIP6) GCMs represent an advancement over previous CMIPs in several aspects, such as enhanced geographical resolution, reduced systematic model biases and uncertainties, and improved simulation of cloud microphysical dynamics (Eyring et al., 2019 ; Kamruzzaman et al., 2023). Nevertheless, the output data generated by GCMs cannot be readily applied to assess the precise effects of CCs on crops at individual sites because of the coarse resolution of GCMs (Wang et al. 2021 ; Islam et al. 2022 ). Downscaling techniques are essential for acquiring high-resolution regional- or local-scale climatic data. Dynamical downscaling or statistical downscaling are the primary methods employed for climatic downscaling. Statistical downscaling is often favored because of its simplicity, affordability, fast calculations, and reduced computational demands (Rashid et al. 2015 ; Islam et al. 2023 ). Several studies have concentrated on the influence of extreme climatic indices, or CCs, on rice yield, primarily based on observed data (Huang et al. 2017 , 2018 ; Abbas et al. 2018 ; Maniruzzaman et al. 2018 ; Vogel et al. 2019 ; Rehmani et al. 2021 ; Fan et al. 2022 ; Song et al. 2022 ), heat stress damage or response mechanisms (Jagadish et al. 2007 ; Das et al. 2014 ; Shi et al. 2015 ; Lawas et al. 2018 ). Jagadish et al. ( 2007 ) conducted greenhouse investigations with different rice genotypes. They found that temperature exposure > 33.7°C at anthesis for < 1 h was enough to cause sterility. Nevertheless, limited studies have explored the projected impacts of extreme climatic indices on rice production worldwide (He et al. 2018 ; Shiru et al. 2022 ; Zhao et al. 2022 ). To our knowledge, no studies have been conducted in Bangladesh to date. Furthermore, the future risk of climate extremes is unpredictable due to the uncertainty introduced by various GCMs and multimodel ensemble (MME) approaches. Thus, assessing the future extreme climate risk for rice using the MME of various GCMs is essential. The northeastern region of Bangladesh, especially in the haor basin, is the primary rice cultivation zone, accounting for approximately 18% of the total rice production of the country (Baishakhy et al., 2023 ; BBS, 2012 ).. The northeastern region's distinctive geographical features and water systems have resulted in various livelihood choices and extensive agricultural yields (Nowreen et al. 2015 ; Kamruzzaman and Shaw 2018). Approximately 85% of the haor basin lands are predominantly dedicated to boro rice cultivation during the dry period, with the remaining 15% designated for rabi crops (Baishakhy et al. 2023 ). Boro rice faces multiple challenges in northeastern regions of Bangladesh, e.g., low-temperature stress at reproductive stages if sowing early, but it is partially safe from flash floods during harvesting; high temperature stress at reproductive stress and flash floods during the maturity stage are challenges for optimum sowing windows (Rashid and Yasmeen 2018 ). Boro crops in the northeastern areas generally reach maturity by the last week of April, coinciding with the usual occurrence of flash floods between mid-April and May in the same area (Ahmed et al., 2017; Roy et al., 2017 ). Due to its heavy reliance on natural conditions, boro rice cultivation is consistently vulnerable to total damage caused by extreme climatic conditions (Nowreen et al., 2015 ). Hence, projecting extreme climate indices is crucial for the northeastern haor basin to assess the future climate risk that can hinder food security. This is the pioneering study in Bangladesh, conducted over the northeast region, to project the future extreme climatic stress on rice cultivation. This work analyses extreme climatic stress that mainly impacts boro rice in the northeastern region. The analysis is focused on using statistically downscaled daily climate data from 15 CMIP6 GCMs and employing the MME technique to ensemble the extreme climatic stress indices derived from the GCM outputs. The primary objectives are (i) to investigate the future trends and potential spatial and temporal shifts in future extreme climatic events for the northeastern part of Bangladesh and (ii) to assess their possible impacts on dry season rice ( Boro ) production in the northeastern part of Bangladesh. 2. Materials and methods 2.1 Study area The northeastern region of Bangladesh, characterized by its distinct hydroecological attributes, consists of extensive bowl-shaped floodplain depressions within the Meghna River basin in the northeast part of Bangladesh (Fig. 1 ). The northeastern haor basin's main portion encompasses four Bangladesh districts: Sunamganj, Sylhet, Habiganj, and Moulavibazar. Additionally, it extends to the Netrokona, Brahmanbaria, and Kishoreganj districts, which lie beyond the major region (Roy et al. 2022 ). Encompassing an expansive 1.99 million hectares (19,998 km2 ) of land, this region serves as a habitat for an estimated population of approximately 19.37 million. According to records, approximately 373 haor or wetland areas, approximately 859,000 hectares, account for approximately 43% of the entire land area within the northeastern districts (CEGISCEGIS, 2012 ). This area has unique hydrological attributes. The annual precipitation exhibits a variation in the range of 2,200 mm near the western periphery and 5,800 mm in the northeastern extremity of the region. In certain catchments that reach India, the headwaters can receive as much as 12,000 mm of precipitation. The primary reason for flash floods in the northeastern region is the water flow from India to Bangladesh (CEGISCEGIS, 2012 ). The rainfall during the winter season in the northeast region is visible due to the influence of CC. The occurrence of dense fog is a common scene during this period. The mean annual maximum and minimum temperatures are 33.30°C and 12.00°C, respectively. The dry season is hot and clear, whereas the rainy season is muggy, uncomfortable, and overcast. The soil in this region mostly consists of alluvial sand, which originates from the Brahmaputra, Meghna, and other interconnected smaller rivers (Haque et al., 2021 ). It has a prolonged dry period lasting approximately six months, followed by a subsequent period of submersion for the remaining months of the year. The cultivable land in this area is utilized throughout the winter to produce boro rice. During the monsoon season, the same region transforms into a breeding ground for open-water fisheries, facilitating the growth of diverse biological species (Roy et al. 2022 ). 2.2 Data sources The weather stations in Bangladesh are unevenly scattered across the country, and there is a limited presence of meteorological observation stations in remote and high-altitude mountainous regions. There are only two meteorological stations in the northeastern region, so ERA5 reanalysis datasets were used to provide gridded climate variable data for this region. Studies have found that ERA5 demonstrated outstanding performance in Bangladesh, as assessed by the different criteria employed for precipitation and temperature (Islam and Cartwright 2020 ; Kamruzzaman et al. 2022 ). This work employed ERA5 datasets (website download link: https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5 ) with a geographical resolution of 0.25°, covering the period from 1995–2014. This research assessed spatiotemporal changes in extreme climatic indices in three future periods: near (2021–2040), mid (2041–2060), and far (2061–2100), utilizing 15 CMIP6 GCMs (Table 1 ) for two SSPs: SSP2-4.5 and SSP5-8.5. The data were accessed from https://esgf-node.llnl.gov/search/cmip6/ . The SSP2-4.5 scenario is moderate, projecting a 2.7°C increase in the global average temperature by 2100. In contrast, SSP5-8.5 represents the most significant alteration. Table 1. List of the global climate models (GCMs) utilized in this study. Serial Model name Institution Resolution longitude × latitude 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 2.81° × 2.79° 4 CNRM-CM6-1 National Centre for Meteorological Research, France 1.41° × 1.40° 5 CNRM-ESM2-1 National Centre for Meteorological Research, France 1.41°×1.40° 6 GFDL-ESM4 NOAA/ Geophysical Fluid Dynamics Laboratory, USA 1.30°×1.00° 7 INM-CM4-8 Institute for Numerical Mathematics, Russia 2.00° × 1.50° 8 INM-CM5-0 Institute for Numerical Mathematics, Russia 2.00° × 1.50° 9 IPSL-CM6A-LR Institut Pierre Simon Laplace, France 2.50° × 1.26° 10 MIROC6 Atmosphere and Ocean Research Institute (The University of Tokyo), National Institute for Environmental Studies, and Japan Agency for Marine-Earth Science and Technology, Japan 1.41° × 1.40° 11 MPI-ESM1-2-LR Max Planck Institute for Meteorology, Germany 1.88° × 1.86° 12 MPI-ESM1-2-HR Max Planck Institute for Meteorology, Germany 1.88° × 1.86° 13 MRI-ESM2-0 Meteorological Research Institute, Japan 1.13° × 1.12° 14 NorESM2-LM Norwegian Climate Center, Norway 2.50° × 1.89° 15 UKESM1-0-LL UK Met Office Hadley Office, UK 1.88° × 1.25° 2.3 Climate Extreme Indices Table 2 provides a comprehensive overview of the extreme climate indices investigated in this research. Our study focuses on five crucial extreme climatic stress indices: three consecutive cold days (CCD3), six consecutive cold days (CCD6), hot days (HD), consecutive hot days (CHD), and heavy rainfall (HR) days. These indices were selected to evaluate their potential impact on boro rice production in northeastern Bangladesh, particularly during the critical reproductive stage, as shown in the generic crop calendar in the northeast region of Bangladesh (Fig. 2 ). Table 2 Definitions of five extreme climate indices were used in this study. Serial Model name Institution Resolution longitude × latitude 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 2.81° × 2.79° 4 CNRM-CM6-1 National Centre for Meteorological Research, France 1.41° × 1.40° 5 CNRM-ESM2-1 National Centre for Meteorological Research, France 1.41°×1.40° 6 GFDL-ESM4 NOAA/ Geophysical Fluid Dynamics Laboratory, USA 1.30°×1.00° 7 INM-CM4-8 Institute for Numerical Mathematics, Russia 2.00° × 1.50° 8 INM-CM5-0 Institute for Numerical Mathematics, Russia 2.00° × 1.50° 9 IPSL-CM6A-LR Institut Pierre Simon Laplace, France 2.50° × 1.26° 10 MIROC6 Atmosphere and Ocean Research Institute (The University of Tokyo), National Institute for Environmental Studies, and Japan Agency for Marine-Earth Science and Technology, Japan 1.41° × 1.40° 11 MPI-ESM1-2-LR Max Planck Institute for Meteorology, Germany 1.88° × 1.86° 12 MPI-ESM1-2-HR Max Planck Institute for Meteorology, Germany 1.88° × 1.86° 13 MRI-ESM2-0 Meteorological Research Institute, Japan 1.13° × 1.12° 14 NorESM2-LM Norwegian Climate Center, Norway 2.50° × 1.89° 15 UKESM1-0-LL UK Met Office Hadley Office, UK 1.88° × 1.25° It is essential to highlight that heavy rainfall can result in flooding, which, in turn, significantly affects rice production. Flooding during harvesting substantially damages crops (Kamruzzaman et al. 2023a , b ). Moreover, high temperatures exceeding 35°C can have detrimental effects on rice production, especially during the flowering and growth stages, leading to reduced rice yields (Das et al. 2014 ; Wang et al. 2019 ). Conversely, critically low temperatures during the reproductive phase can also harm rice production (Kabir et al. 2016 ). 2.4 Methodologies 2.4.1 Downscaling and bias correction The raw GCM simulations obtained from data extraction were first interpolated to match observed locations. Subsequently, simple quantile mapping (SQM) was utilized to fix the bias in the distribution of GCM simulations, aligning them with the distribution of observed ERA5 data at each respective site. It is worth noting that the nonparametric empirical formula employed in quantile mapping (QM) proves more skillful at reducing systematic bias compared to parametric approaches (Gudmundsson et al. 2012). The bias-corrected GCM data generated by QM closely align with the observed distribution, significantly bolstering the dependability of climate predictions (Heo et al. 2019 ). As a result, this approach has gained widespread adoption for downscaling GCM simulations (Pierce et al. 2015 ; Alamgir et al. 2019 ). In this research, daily precipitation, maximum temperature (Tmax), and minimum temperature (Tmin) data for the years spanning from 1995–2100 were downscaled to a standard resolution (0.25) by utilizing the SQM method. Subsequently, bias correction was performed based on ERA5 grids, employing the rSQM software developed by Cho et al. ( 2018 ). This study utilized a three-step process for bias correction. First, each target location's global climate model (GCM) data were obtained. Second, an assessment of the inherent biases within the GCM data was conducted. Finally, bias correction methods were applied to the projections. Differences in cumulative distribution functions (CDFs) between observed and simulated data for the retrospective period were calculated and subsequently used to adjust future simulations for a specified percentile, as detailed in Eq. ( 1 ). $${x}_{p}^{{\prime }}\left(t\right)={x}_{p}\left(t\right)+{F}_{obs}^{-1}\left({F}_{p.sim}\right({x}_{p}\left(t\right))-{F}_{r.sim}^{-1}({F}_{p.sim}\left({x}_{p}\left(t\right)\right)$$ 1 In this context, \(F\left(\theta \right)\) and \({F}^{-1}\left(\theta \right)\) represent the CDF and its inverse for the daily data θ, respectively, while \({x}_{p}^{{\prime }}\left(t\right)\) and \({x}_{p}\left(t\right)\) signify the bias-corrected and raw future projections on day t. The subscripts \(p.sim\) , \(r.sim\) , and \(obs\) denote the future prediction, retrospective simulation, and daily observed data, respectively (Kamruzzaman et al. 2019 ). 2.4.2 Multimodel ensemble (MME) approach The extreme climate indices were computed using 15 different downscaled CMIP6 GCM rainfall, Tmax, and Tmin values over the historical period spanning from 1995 to 2014. The inherent uncertainty in climate modeling is a fundamental characteristic resulting from the intricate nature of the Earth's climate system and the constraints of scientific comprehension and data availability. This work constructed an MME by finding the average of the projections from 15 CMIP6 GCMs for both the historical and future. This was done to reduce the projections' uncertainty and better understand how each index has changed. The calculation of the MME was conducted for three future periods: near (2021–2040), mid (2041–2060), and far (2061–2100). To calculate changes, the study compared the anticipated MME to the historical downscaled MME for future periods. This work also examined the spatiotemporal aspects of these alterations to determine locations more susceptible to the consequences of CCs. 2.4.3 Modified Mann–Kendall (MMK) test Trend analysis is an essential technique for comprehending alterations in hydroclimatic datasets. The present study employed the modified Mann–Kendall (MMK) test proposed by Hamed and Ramachandra Rao ( 1998 ), which considers autocorrelation when calculating variance for climatic datasets. The following equation is employed to calculate autocorrelation in the data: $${V}^{*}\left(S\right)=var\left(S\right).\frac{n}{{n}_{s}^{*}}=\frac{n(n-1)(2n+5)}{18}.\frac{n}{{n}_{s}^{*}}$$ 2 where n/ \({n}_{s}^{*}\) denotes an adjustment caused by autocorrelation in the data. The empirical formula offers the most precise estimation of the theoretical values derived from n/ \({n}_{s}^{*}\) expressed as follows: $$\frac{n}{{n}_{s}^{*}}=1+\frac{2}{n(n-1)(n-2)}\times \sum _{i=1}^{n-1}(n-i)(n-i-1)(n-i-2){\rho }_{s}\left(i\right)$$ 3 where n represents the actual observation number and \({\rho }_{s}\left(i\right)\) is the autocorrelation function of the observation rankings. The approximation facilitates evaluating the variance of S, considering the observation rankings without using data normalization or autocorrelation functions (Hamed and Rao 1998 ). 3. Results 3.1 Bias-corrected Outputs Evaluation The comparison of downscaled precipitation, Tmax, and Tmin of the ensemble mean of 15 CMIP6 GCMs is presented in Fig. 3 . The data were evaluated before and after bias correction and assessed with observed values. The ensemble mean raw GCMs exhibited considerable dry biases across the year except for the winter (DJF) months in mean monthly rainfall. Moreover, the analysis revealed the presence of significant warm biases in Tmax and Tmin, particularly during the premonsoon (MAM) and monsoon (JJAS) seasons. The raw ensemble mean tended to underestimate the observed rainfall during the months of MAM and JJAS by approximately 54.33–387.24 and 98.97–398.68 mm/month, respectively. Conversely, the ensemble mean overestimated the rainfall from December to January by approximately 7.91–17.01 mm/month. The raw simulated Tmax overestimated the MAM and JJAS months by approximately 1.66°C–4.18°C and 0.94°C–3.75°C, respectively, whereas November to January was underestimated by approximately 0.48°C–1.48°C. Additionally, the raw simulated Tmin tended to underestimate the observed Tmin from April to September by a range of 0.80°C–2.13°C. The approach of bias correction aims to reduce these errors. The present work utilized an SQM technique for bias correction. This correction led to a more pronounced alignment between the monthly average data and the observed values. After applying the SQM bias correction technique, a significant improvement was noticed for rainfall, where R 2 increased from 0.68 to the perfect 1. Similarly, bias was reduced for Tmax and Tmin and improved R 2 . 3.2 Trends in extreme climate indices The MMK trends of severe climate indicators for historical and projected SSP2-4.5 and SSP5-8.5 scenarios over Bangladesh from 2020–2100 are presented in Table 3 . For the historical period, only CCD3 exhibited a significant decreasing trend, while all other extreme climatic indices showed an insignificant decreasing trend except for HR days. Table 3 Assess Modified Mann-Kendall (MMK) trends for extreme climate indices during the historical period (1995–2014) and the projected period (2020–2100). Variables Historical SSP2-4.5 SSP5-8.5 SS Zc SS Zc SS Zc CCD3 –0.094 –2.37 * –0.030 –6.03 * –0.035 –4.83 * CCD6 –0.064 –1.72 –0.023 –6.68 * –0.024 –5.01 * HD –0.004 –0.59 0.112 7.94 * 0.241 6.05 * CHD –0.011 –1.51 0.061 7.80 * 0.136 5.77 * HR 0.023 1.46 0.001 0.66 –0.001 –0.37 CCD3, three consecutive cold days; CCD6, six consecutive cold days; HD, hot days; CHD, consecutive hot days; HR, heavy rainfall days; SS, Sen’s Slope; Zc, Kendall's test statistics. Asterisks designate significance level where * indicate p-values of < 0.01. The projected (2020–2100) period results displayed significant decreasing trends in CCD3 and CCD6 and significantly increasing trends in HD and CHD for SSP2-4.5 and SSP5-8.5. However, no important trends were detected for HR days. The rates of decline in CCD3 were observed to be 0.30 and 0.35 days/decade for SSP2-4.5 and SSP5-8.5, respectively. In the case of CCD6, the decreasing rates were 0.23 and 0.24 days/decade for SSP2-4.5 and SSP5-8.5, respectively. The increase in HD was identified at a rate of 1.12 days/decade for SSP2-4.5 and 2.41 days/decade for SSP5-8.5. The rise in CHD stands at 0.61 days/decade and 1.36 days/decade for SSP2-4.5 and SSP5-8.5, respectively. Both scenarios detected an insignificant rise in HR at 0.01 days/decade. 3.3 Spatiotemporal changes in extreme indices 3.3.1 Cold stress indices 3.3.1.1 Three Consecutive Cold Days Figure 4 depicts the historical and predicted variation in the occurrence of three consecutive cold days (CCD3) in the northeastern region, considering various scenarios and future timeframes compared to the reference period from 1995 to 2014. During the historical period, the total CCD3 ranged between 0.76 and 9.94 days over the study area. The historical CCD3 was greater in the northeastern region and lower toward the southern part (Fig. 4 a). The study demonstrates that across all projected periods and SSPs, there is a noticeable negative shift in CCD3. There is a notable decrease (up to approximately − 9 days for SSP5-8.5) in CCD3 in both scenarios in the northeastern region of the northeast haor basin, including the Sylhet, Sunamgonj, and partly Netrakona districts. On the other hand, a relatively lower projected decrease in CCD3 was noticed in the southern region (Fig. 4 b). For SSP2-4.5, the MME estimates a decrease in CCD3 of − 0.84 days (–0.30 to − 1.72) in the near future, − 1.71 days (–0.52 to − 4.28) in the mid-future, and − 2.52 days (–0.72 to − 6.59 days) in the far future. Similarly, for SSP5-8.5, the estimated changes in CCD3 range from − 0.77 days (–0.28 to − 1.65 days) in the near future, − 2.09 days (–0.55 to − 5.17 days) in the mid-future, and − 3.06 days (–0.75 to − 8.78 days) in the far future. The projected CCD3 changes exhibited a greater declining pattern in the far future than in the near and mid future (Fig. 4 c). 3.3.1.2 Six consecutive cold days Figure 5 illustrates the historical and projected spatiotemporal changes in Six Consecutive Cold Days (CCD6) for the near, mid and far futures under the scenarios SSP2-4.5 and SSP5-8.5. The estimated historical (1995–2014) CCD6 throughout the northeastern region exhibited a range of 0.76 days to 9.94 days. Figure 5 a displays the historical concentration of CCD6, revealing a notable spike in the northeast region, which weakens as one moves toward the central, northwestern, and southern regions. Similar to CCD3, CCD6 also displays an apparent decline in all anticipated periods and SSPs over the northeastern region. The region that might experience the most significant decrease (up to approximately − 9 days for SSP5-8.5) in CCD6 was identified as the northeastern region (Sylhet and Sunamgonj districts). In contrast, the southern region showed relatively lower increases (Fig. 5 b). The CCD6 was projected to decrease by an average of − 0.72 days (0 to − 1.92 days) in the near period, − 1.41 days (–0.15 to − 4.69 days) in the mid period, and − 2.04 days (–0.23 to − 7.09 days) in the far period for SSP2-4.5. Likewise, for SSP5-8.5, the projected changes in CCD6 are an average of − 0.63 days (–0.08 to − 1.81 days) in the near period, − 1.69 days (–0.13 to − 5.74 days) in the mid period, and − 2.37 days (–0.24 to − 8.73 days) in the far period. This result revealed that anticipated changes in CCD6 projections demonstrate a more pronounced decline in the far future relative to the near and mid futures, which are identical to the CCD3 changes (Fig. 5 c). 3.3.2 Heat stress indices 3.3.2.1 Hot days Figure 6 depicts the historical and projected spatiotemporal changes in hot days (HD) for the near, mid, and far futures under the SSP2-4.5 and SSP5-8.5 scenarios in the northeastern region. During the historical period, the HD frequency ranged from 0–5.91 days over the study area. A higher HD was observed in the southwestern region (Kishoreganj and Brahmanbaria districts), while it was relatively lower in the northeastern region (Sylhet and Sunamgonj districts) of the study area (Fig. 6 a). The HD increased in all periods and scenarios. For both scenarios, a noticeable increase in HD was observed in the southwestern part of the northeastern region (Kishoreganj and Brahmanbaria districts). In contrast, the northeast region (Sylhet and Sunamgonj districts) experienced relatively lower rises (Fig. 6 b). In the context of SSP2-4.5, the projected increase in HD is estimated to be an average of 0.61 days (0–1.17 days) in the near future, 2.44 days (0.02–4.42 days) in the mid-future, and 6.26 days (0.37–10.40 days) in the far future. Similarly, for SSP5-8.5, the estimated changes in HD are an average of 0.62 days (0.02–1.18 days) in the near future, 3.95 days (0.11–6.93 days) in the mid-future, and 12.04 days (1.40–17.80 days) in the far future. The projected HD changes revealed a greater increasing pattern in the far future than in the near and mid future (Fig. 6 c). 3.3.1.2 Consecutive hot days (CHD) Figure 7 exhibits the historical and predicted spatial and temporal changes in consecutive hot days (CHDs) all over the northeastern region for the near, mid, and far future periods for the SSP2-4.5 and SSP5-8.5 scenarios. Throughout the historical era, the frequency of CHD varied between 0 and 3.08 days. The study area had a greater CHD in the southwestern region, namely, in the districts of Kishoreganj and Brahmanbaria. Conversely, the northeastern region, encompassing the districts of Sylhet and Sunamgonj, displayed a comparatively lower CHD (Fig. 7 a). This study expects CHD to increase in both periods and scenarios. For both scenarios, a notable rise in HD was observed in the southwestern (Kishoreganj and Brahmanbaria districts) area of the northeastern haor basin. In contrast, the northeastern part (Sylhet and Sunamgonj districts) showed relatively lower increases (Fig. 7 b). The CHD was projected to increase by an average of 0.38 days (0–0.83 days) in the near period, 1.31 days (0–2.46 days) in the mid period, and 3.52 days (0.19–6.02 days) in the far period for SSP2-4.5. Similarly, the expected increases in CHD for SSP5-8.5 were an average of 0.34 days (0.01–0.78 days) in the near period, 2.15 days (0.06–3.76 days) in the mid period, and 6.85 days (0.72–10.48 days) in the far period. The results of this study demonstrate that anticipated changes in CHD projections exhibit a more noticeable increase in the far future than in the near and mid futures, which aligns with HD changes (Fig. 7 c). 3.3.3 Heavy rainfall days Figure 8 presents the historical projected changes in heavy rainfall (HR) days over the northeastern region under different scenarios and future periods. The estimated historical HR over the northeast region ranged from 1.70 to 2.88 days. The higher HR is observed mostly in the Sylhet and partly in the Sunamgonj and Habiganj districts in our study area's central and northeastern parts. In contrast, a lower HR is observed in the southern and western regions in the study area (Fig. 8 a). For all periods and scenarios, the anticipated HR showed a minimal (up to − 0.20 to 0.30 days) change with a mix of increasing and decreasing patterns, indicating that climate change would probably have little impact on the HR in the study area. An increase in HR was noticed in the northeastern region, while there was a decrease in the western region for both SSPs in the near future. In the mid-future, the central to southern regions showed a higher increase for SSP2-4.5. However, it was narrowed to the central region for SSP5-8.5. A higher increase in HR was noticed in the northeastern region for SSP2-4.5, while it was shifted to the rest of the regions for SSP5-8.5 for the far future (Fig. 8 b). The projected mean changes for SSP2-4.5 in the near, mid, and far periods were − 0.02, − 0.08, and − 0.01 days, respectively, while those for SSP5-8.5 were 0.09, 0.05, and 0.11, respectively (Fig. 8 c). 4. Discussions Temperature is a significant environmental factor that plays a crucial role in influencing plant growth, development, and yield. Global warming poses a severe threat to the productivity of crops on a global scale by prolonged exposure to temperatures above the optimal range for plant growth (Janni et al. 2020 ; Xu et al. 2021 ). The present study examined future trends and anticipated alterations in five extreme thermal stress (CCD3, CCD6, HD, CHD, and HR) indices. These findings were based on MME averages derived from 15 GCM simulations of rainfall, Tmax, and Tmin under the CMIP6 framework for both medium (SSP2-4.5) and high (SSP5-8.5) scenarios. Indicators linked to heat stress are deemed more definite and immediate because of their direct association with temperature. These indicators significantly influence agricultural productivity, mainly rice, especially in the northeastern regions of Bangladesh, such as the Haor B asin. This area is heavily dependent on rice production and might be particularly susceptible to changes in extreme climatic indices. Evaluating these alterations can provide a precise understanding of extreme climatic conditions in a specific environment, facilitating a deeper comprehension of potential consequences and adaptation prospects. We found that there was a noteworthy increasing trend in extreme heat stress (HD and CHD) indices and a noteworthy decreasing trend in cold stress (CCD3 and CCD6) indices over the northeastern haor basin from 2020–2100 for both the SSP2-4.5 and SSP5-8.5 scenarios. The study area had no significant trend in the projected HR days index. A similar representation was noticed in projected changes in extreme climatic stress indices for all periods and scenarios. This situation suggests an overall warming in the future northeastern haor basin climate, as expected, as shown in previous studies by the MME of CMIP5 (Alamgir et al. 2019 ; Islam et al. 2023 ) and CMIP6 (Kamruzzaman et al. 2023c ) GCMs over the entirety of Bangladesh. Rice plants are more vulnerable to heat stress during the reproductive stage, which includes panicle initiation, flowering, development of male and female gametophytes, anthesis including pollination, and fertilization, compared to the vegetative stage (Arshad et al. 2017 ; Jagadish et al. 2021 ; Xu et al. 2021 ). The reproductive phase of dry season rice cultivation in the northeastern region of Bangladesh primarily occurs in mid-March and April, overlapping with the optimum temperatures (38°C − 41°C) observed throughout the year (Siddik and Rahman 2014 ). Moreover, the projected heat stress increases in this time would increase the temperature in the northeastern region, which might exacerbate adverse impacts. Heat stress hinders the process of panicle initiation and the development of spikelets, resulting in the deformation of floral organs and a decrease in both the size and number of spikelets in rice (Cao et al. 2008 ; Xu et al. 2020 ), which might impact the hoar region of rice plants in the future. Elevated temperatures hinder the process of anther development, resulting in a decrease in the functionality and germination of pollen (Tang et al. 2008 ; Wang et al. 2019 ), which might be attributed to the inhibited growth of pollen mother cells, unusual breakdown of the tapetum, inadequate nutrient accumulation, and restricted sugar movement to the pollen (Endo et al. 2009 ; Rezaul et al. 2019 ). Our study found a higher increase in HD and CHD in the northeastern region of Bangladesh, which has detrimental impacts on the flowering stage, as it is susceptible to extreme temperature stress (Satake and Yoshida 1978 ; Yoshida and Nagato 2011 ). The period of anthesis in rice is considered crucial when it faces heat stress, where processes including dehiscence of anthers, pollination, germination of pollen, and pollen tube development are influenced within 45 minutes after spikelet opening (Prasad et al. 2006 ; Arshad et al. 2017 ). Prior research has demonstrated that heat stress negatively affects the fertility of rice spikelets when exposed to temperatures over 35°C for approximately 5 days during the flowering stage at anthesis, resulting in decreased or no yield (Satake and Yoshida 1978 ; Jagadish et al. 2007 ). Elevated temperatures lead to a reduction in the fertility of rice spikelets by diminishing the viability of pollen, hindering the dehiscence of anthers, and obstructing the germination of pollen tubes (Das et al. 2014 ; Shrestha et al. 2022 ). Wang et al. ( 2019 ) found a considerable decrease in spikelet fertility of single-season rice at 38°C compared to 32°C and 35°C for 3 days or more in China. The vulnerability of rice to heat stress in the future world with higher temperatures underscores the need to prioritize targeted adaptations or mitigation methods, particularly in the southern to southwestern regions (Brahmanbaria and Kishoreganj) of the northeastern haor basin, where heat is projected to become more prominent. Choosing heat-tolerant cultivars can significantly mitigate rice yield reductions resulting from heat stress. Nevertheless, heat-resistant cultivars are still susceptible to heat stress hazards when temperatures exceed their tolerance limit due to temperature instabilities. Numerous crop management practices, such as agronomic management (changing the planting time of rice, applying growth regulators, and mist spray therapy) (Wu et al. 2016 ; Khan et al. 2019 ; Wu and Yang 2019 ; Jiang et al. 2020 ), avoiding heat by initiating flowering in the early morning (Jagadish et al. 2008 ; Julia and Dingkuhn 2012 ; Hirabayashi et al. 2015 ), enhancing thermotolerance through conventional breeding (Driedonks et al. 2016 ; Kilasi et al. 2018 ; Raza et al. 2020 ), thermotolerance breeding by discovering heat resistance genes (Wei et al. 2013 ; Liu et al. 2016 ), transgenic strategies (Rerksiri et al. 2013 ; Shen et al. 2015 ; Liu et al. 2018 ), and genome editing techniques (Qiu et al. 2018 ; Gao 2019 ; Wang et al. 2020 ), have demonstrated efficacy in mitigating or preventing heat-induced harm in rice. However, modification of the planting schedule is difficult for the northeastern haor basin due to the late vacation of land from rainy season water stagnation and the higher risk of floods and flash floods if plating is delayed. 5. Implication of the findings 5.1 Practical implications Crop management strategies The insights from the study underscore the immediate need for practical crop management strategies in the northeastern haor basin of Bangladesh. Farmers can benefit considerably by adjusting rice planting schedules, opting for heat-tolerant rice varieties, and incorporating agronomic practices that mitigate the impact of escalating heat stress during crucial reproductive stages. Technology adoption for climate resilience Practical implications extend to adopting technology-driven solutions, including precision agriculture tools and real-time weather forecasting. These technologies empower farmers to make informed decisions on irrigation, pest control, and other intercultural activities, enhancing the overall resilience of rice cultivation amidst evolving climate conditions. Government policies and support Policymakers should consider practical interventions such as financial incentives and subsidies to motivate farmers to adopt climate-resilient practices. Investments in research and development for heat-resistant rice varieties and the development of climate-responsive infrastructure are vital elements of government support to fortify agricultural resilience. Educational programs and extension services Implementing extension services and educational programs emerges as a practical avenue to raise awareness among haor area farmers. Equipping farmers with the requisite knowledge and skills through educational initiatives empowers them to implement practical measures for sustainable rice cultivation in the face of changing climatic conditions. 5.2 Theoretical implications: Advancement in climate modeling techniques The study contributes theoretically by utilizing advanced climate modeling techniques, specifically the CMIP6 framework. This enhances the theoretical understanding of applying climate models to project future scenarios, providing more precise predictions of climate change impacts on rice cultivation in specific areas. Validation and refinement of climate change hypotheses Theoretical implications include the validation and refinement of hypotheses related to the impact of global warming on thermal stress indices. Our findings strengthen existing theories about the consequences of climate change, offering empirical evidence for the projected increase in extreme heat stress. Integration of multiple climate scenarios The study considers that both medium (SSP2-4.5) and high (SSP5-8.5) emission scenarios contribute theoretically by acknowledging the uncertainty in future climate conditions. This aligns with the theoretical understanding that climate projections should encompass different potential trajectories based on socioeconomic factors, providing a comprehensive basis for policy formulation. Insights into reproductive stage vulnerability Theoretical implications extend to a deeper understanding of the vulnerability of rice plants during the reproductive stage. The study contributes theoretical insights into the intricate relationship between temperature dynamics and crop yield by highlighting specific panicle initiation, flowering, and pollination stages as highly susceptible to heat stress. In summary, the practical implications offer actionable steps for immediate implementation, while the theoretical implications contribute to the broader scientific understanding of climate change dynamics and modeling techniques. Both aspects are crucial for developing effective strategies to address the challenges posed by changing climate conditions in the northeastern region of Bangladesh. 6. Limitations and Future Research Direction This study only considered historical phenological knowledge to compute future extreme climatic indices for the reproductive stage of rice in the northeastern region of Bangladesh. It did not address the potential alterations in rice phenology due to a warmer environment and agronomic practices in the future. Furthermore, our study did not predict rice yield in future situations under changing climate scenarios. The projection of future phenology, timing, and results relies on implementing appropriate management policies according to how climate change emerges. Additional research is needed to utilize a phenological model to accurately predict the impact of a warmer climate and agronomic techniques on rice phenology. Moreover, it is necessary to explore the alteration of climatic extremes considering the various growth stages in the northeastern haor basin of Bangladesh. Hence, our findings may exaggerate the adverse consequences of heat extremes while underestimating the negative impacts of cold extremes. Flash floods are a major concern for rice production during the northeastern region's premonsoon (MAM) season (Nowreen et al. 2015 ). In this study, we used an extreme rainfall index called HR days (> 20 mm/day) but found no significant change. The northeastern part of Bangladesh is encompassed by the mountain regions Meghalaya, Tripura, and Assam of India, with many of the most precipitation-rich locations on the planet within a few hundred kilometers of its boundary. Studies have explored whether flash floods in the northeastern Haor basin resulted from a rapid influx of water into the rivers due to the heavy precipitation over a shorter period in the basin's upper part (Dey et al. 2021 ). Future research endeavours should prioritize evaluating extreme rainfall indices within both the upper and lower Meghna River catchments to address the issue of flash floods more effectively. 7. Conclusion The northeastern part of Bangladesh, known for its unique hydroecological attributes, faces significant challenges in changing climate patterns. This study's analysis of extreme climatic indices during the rice anthesis and grain filling stage reveals a trend of increasing heat stress, which could adversely affect boro rice production in Bangladesh, a vital agricultural activity in the region. The reproductive phase of rice is susceptible to extreme temperatures, and the projected rise in heat stress indices during this critical phase could lead to reduced yields. To address these challenges, it is imperative to prioritize adaptive measures. Heat-tolerant rice cultivars and adjustment of planting time through agronomic management practices could help mitigate the adverse impacts of rising temperatures on rice production in the northeastern region of Bangladesh. Additionally, further research is needed to develop comprehensive phenological models that consider both climatic extremes and agronomic practices for a more accurate prediction of future rice yields. Furthermore, the study did not find significant changes in heavy rainfall days despite the region's susceptibility to flash floods during the premonsoon season. Future research should explore extreme rainfall indices in both the upper and lower Meghna River catchments to better understand and address the issue of flash floods in the northeastern part of Bangladesh. However, this research underscores the importance of proactive adaptation strategies in the northeastern region of Bangladesh to secure the livelihoods of its inhabitants, who heavily rely on rice production, and to ensure food security in the face of changing climate patterns. Declarations Acknowledgment : We acknowledge the Cereal Systems Initiative for South Asia project (https://csisa.org) funded by the United States Agency for International Development (USAID) for assisting in preparing this manuscript. Author contributions Mohammad Kamruzzaman: Conceptualization, Methodology, Software. HM Touhidul Islam: Data curation, Visualization, Investigation Writing- Original draft preparation. Md. Sazzadur Rahman : Supervision Sharif Ahmed: Software, Review and Editing and Validation. Liala Ferdousi Lipi, Md. Arifur Rahman Khan, Lam-Son Phan Tran, and AMK Zakir Hossain: Writing- Reviewing and Editing Funding statement This research received no specific grant from funding agencies in public, commercial, or not-for-profit sectors. Ethics approval Not applicable. Consent to participate Not applicable. Consent for publication Not applicable. Data availability In response to a formal request, we will provide the requested data. Declaration of interest statement The authors declare no conflicts of interest. References Abbas F, Rehman I, Adrees M, et al (2018) Prevailing trends of climatic extremes across Indus-Delta of Sindh-Pakistan. 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Heliyon 8 Siddik MAZ, Rahman M (2014) Trend analysis of maximum, minimum, and average temperatures in Bangladesh: 1961-2008. Theor Appl Climatol 116:. https://doi.org/10.1007/s00704-014-1135-x Song Y, Wang C, Linderholm HW, et al (2022) The negative impact of increasing temperatures on rice yields in southern China. Science of the Total Environment 820:. https://doi.org/10.1016/j.scitotenv.2022.153262 Tang RS, Zheng JC, Jin ZQ, et al (2008) Possible correlation between high temperature-induced floret sterility and endogenous levels of IAA, GAs and ABA in rice ( Oryza sativa L.). Plant Growth Regul 54:. https://doi.org/10.1007/s10725-007-9225-8 Uddin MT, Dhar AR (2018) Government input support on Aus rice production in Bangladesh: Impact on farmers’ food security and poverty situation. Agric Food Secur. https://doi.org/10.1186/s40066-018-0167-3 Vogel E, Donat MG, Alexander L V., et al (2019) The effects of climate extremes on global agricultural yields. Environmental Research Letters 14:. https://doi.org/10.1088/1748-9326/ab154b Wang B, Zhong Z, Wang X, et al (2020) Knockout of the OsNAC006 transcription factor causes drought and heat sensitivity in rice. Int J Mol Sci 21:. https://doi.org/10.3390/ijms21072288 Wang X, Hou X, Piao Y, et al (2021) Climate Change Projections of Temperature Over the Coastal Area of China Using SimCLIM. Front Environ Sci 9:. https://doi.org/10.3389/fenvs.2021.782259 Wang Y, Wang L, Zhou J, et al (2019) Research Progress on Heat Stress of Rice at Flowering Stage. Rice Sci 26 Wei H, Liu J, Wang Y, et al (2013) A dominant major locus in chromosome 9 of rice ( Oryza sativa L.) confers tolerance to 48°C high temperature at the seedling stage. Journal of Heredity 104:. https://doi.org/10.1093/jhered/ess103 Wu C, Cui K, Wang W, et al (2016) Heat-induced phytohormone changes are associated with disrupted early reproductive development and reduced yield in rice. Sci Rep 6:. https://doi.org/10.1038/srep34978 Wu YS, Yang CY (2019) Ethylene-mediated signaling confers thermotolerance and regulates transcript levels of heat shock factors in rice seedlings under heat stress. Bot Stud 60:. https://doi.org/10.1186/s40529-019-0272-z Xu J, Henry A, Sreenivasulu N (2020) Rice yield formation under high day and night temperatures—A prerequisite to ensure future food security. Plant Cell Environ 43 Xu Y, Chu C, Yao S (2021) The impact of high-temperature stress on rice: Challenges and solutions. Crop Journal 9 Yoshida H, Nagato Y (2011) Flower development in rice. J Exp Bot 62 Zhao W, Chou J, Li J, et al (2022) Impacts of Extreme Climate Events on Future Rice Yields in Global Major Rice-Producing Regions. Int J Environ Res Public Health 19:. https://doi.org/10.3390/ijerph19084437 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 06 Dec, 2024 Read the published version in Theoretical and Applied Climatology → Version 1 posted Reviews received at journal 08 Jul, 2024 Reviews received at journal 03 Jul, 2024 Reviewers agreed at journal 02 Jul, 2024 Reviewers agreed at journal 30 Jun, 2024 Reviewers agreed at journal 29 Jun, 2024 Reviewers agreed at journal 29 Jun, 2024 Reviewers agreed at journal 28 Jun, 2024 Reviewers agreed at journal 27 Jun, 2024 Reviewers agreed at journal 27 Jun, 2024 Reviewers agreed at journal 25 Apr, 2024 Reviewers agreed at journal 09 Mar, 2024 Reviewers invited by journal 09 Mar, 2024 Editor assigned by journal 03 Mar, 2024 Submission checks completed at journal 03 Mar, 2024 First submitted to journal 02 Mar, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4007462","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":276169555,"identity":"beb26b54-ebf1-4e83-8e0a-b4b83340fdbf","order_by":0,"name":"Mohammad Kamruzzaman","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxklEQVRIiWNgGAWjYDACZuYGBsYGBgY2diDBYGBBjBZGqBaeAyAtEsRYA9XCIJEA4hGhRbedsfFx4Y57+XySz69u+FEgwcDf3p2AV4vZYcZm45lnii3bpHPKbvYAHSZx5uwGQlrapHnbEgzYpHPSbvAAtRhI5BKrRfJM2s0/pGmRYD92m1hbmo15zwC18OSw3ZYxkOAh7Jfzhw8+5t2RYCDffvzZzTd/bOT423vxa0ECPAZgkljlIMD+gBTVo2AUjIJRMIIAANSQQa1L6jmfAAAAAElFTkSuQmCC","orcid":"","institution":"Bangladesh Rice Research Institute","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"","lastName":"Kamruzzaman","suffix":""},{"id":276169556,"identity":"1450f1a6-afb6-45e1-94c6-cca3f5cf94f5","order_by":1,"name":"HM Touhidul Islam","email":"","orcid":"","institution":"Begum Rokeya University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"HM","middleName":"Touhidul","lastName":"Islam","suffix":""},{"id":276169557,"identity":"0bd58d9c-b002-4f51-8616-614e19bee391","order_by":2,"name":"Md. Sazzadur Rahman","email":"","orcid":"","institution":"Bangladesh Rice Research Institute","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Md.","middleName":"Sazzadur","lastName":"Rahman","suffix":""},{"id":276169558,"identity":"bc6496e3-fabb-4854-880d-2322222caf60","order_by":3,"name":"Sharif Ahmed","email":"","orcid":"","institution":"International Rice Research Institute, Bangladesh Office","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sharif","middleName":"","lastName":"Ahmed","suffix":""},{"id":276169559,"identity":"e9a4fe57-eaa0-4bc3-b761-d4a624a5a906","order_by":4,"name":"Liala Ferdousi Lipi","email":"","orcid":"","institution":"Bangladesh Rice Research Institute","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Liala","middleName":"Ferdousi","lastName":"Lipi","suffix":""},{"id":276169560,"identity":"775c546a-db46-479d-ae3b-b7e97e81d6de","order_by":5,"name":"Md. Arifur Rahman Khan","email":"","orcid":"","institution":"Bangabandhu Sheikh Mujibur Rahman Agricultural University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Md.","middleName":"Arifur Rahman","lastName":"Khan","suffix":""},{"id":276169561,"identity":"e40161c1-a773-40ed-8040-88815f73eb84","order_by":6,"name":"Lam-Son Phan Tran","email":"","orcid":"","institution":"Texas Tech University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lam-Son","middleName":"Phan","lastName":"Tran","suffix":""},{"id":276169562,"identity":"0a830940-d446-4624-901c-a24e2aa6ab22","order_by":7,"name":"AMK Zakir Hossain","email":"","orcid":"","institution":"Bangladesh Agricultural University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"AMK","middleName":"Zakir","lastName":"Hossain","suffix":""}],"badges":[],"createdAt":"2024-03-03 03:59:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4007462/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4007462/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00704-024-05270-5","type":"published","date":"2024-12-06T15:57:46+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":52101385,"identity":"0ee28fe9-124d-479d-bbc7-a3fe5a881bcc","added_by":"auto","created_at":"2024-03-06 19:04:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1335739,"visible":true,"origin":"","legend":"\u003cp\u003eDigital elevation map (DEM) with grid points of the northeastern \u003cem\u003ehaor \u003c/em\u003eregion of Bangladesh.\u003c/p\u003e","description":"","filename":"Figures1.png","url":"https://assets-eu.researchsquare.com/files/rs-4007462/v1/ce1bca3dbb106467d4b1024e.png"},{"id":52101386,"identity":"cc91e77c-09de-46a9-bcc4-87c19938d9bc","added_by":"auto","created_at":"2024-03-06 19:04:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":269787,"visible":true,"origin":"","legend":"\u003cp\u003eGeneric rice crop calendar in the northeastern \u003cem\u003ehaor\u003c/em\u003e region of Bangladesh.\u003c/p\u003e","description":"","filename":"Figures2.png","url":"https://assets-eu.researchsquare.com/files/rs-4007462/v1/c29bc9597c2dc1ed30c366ee.png"},{"id":52101387,"identity":"e7144066-c477-4289-b864-aad44db89edc","added_by":"auto","created_at":"2024-03-06 19:04:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":741177,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of observed data with before and after bias correction historical GCM rainfall, Tmax and Tmin for the period 1995–2014. Tmax, Tmin, Obs and CMIP6 indicate maximum temperature, minimum temperature, observation and coupled model intercomparison project phase 6, respectively.\u003c/p\u003e","description":"","filename":"Figures3.png","url":"https://assets-eu.researchsquare.com/files/rs-4007462/v1/941695e8db52ca46642159e4.png"},{"id":52101388,"identity":"47fc2ac1-9876-4e6f-bdca-69907eab5683","added_by":"auto","created_at":"2024-03-06 19:04:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":575011,"visible":true,"origin":"","legend":"\u003cp\u003eThe spatial (a) historical, (b) projected, and (c) temporal changes in three consecutive cold days (CCD3) for SSP2-4.5 and SSP5-8.5 in the near, mid, and far futures.\u003c/p\u003e","description":"","filename":"Figures4.png","url":"https://assets-eu.researchsquare.com/files/rs-4007462/v1/d2b91cf2c962d693ff7e735e.png"},{"id":52101391,"identity":"ee6c3657-f3c9-4157-9b11-22e8b62bd225","added_by":"auto","created_at":"2024-03-06 19:04:42","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":610048,"visible":true,"origin":"","legend":"\u003cp\u003eThe spatial (a) historical, (b) projected, and (c) temporal changes in six consecutive cold days (CCD6) for SSP2-4.5 and SSP5-8.5 in the near, mid, and far futures.\u003c/p\u003e","description":"","filename":"Figures5.png","url":"https://assets-eu.researchsquare.com/files/rs-4007462/v1/1616615a156e487680b536d8.png"},{"id":52101393,"identity":"09da371f-d235-4794-9fc0-03fa14d3288d","added_by":"auto","created_at":"2024-03-06 19:04:43","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":602367,"visible":true,"origin":"","legend":"\u003cp\u003eThe spatial (a) historical, (b) projected, and (c) temporal changes in hot days (HD) for SSP2-4.5 and SSP5-8.5 in the near, mid, and far futures.\u003c/p\u003e","description":"","filename":"Figures6.png","url":"https://assets-eu.researchsquare.com/files/rs-4007462/v1/a11986a8fd3a7b68da447ff5.png"},{"id":52101745,"identity":"6980d395-f2fc-4328-a850-31d8d0889bff","added_by":"auto","created_at":"2024-03-06 19:12:42","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":589229,"visible":true,"origin":"","legend":"\u003cp\u003eThe spatial (a) historical, (b) projected, and (c) temporal changes in consecutive hot days (CHD) for SSP2-4.5 and SSP5-8.5 in the near, mid, and far futures.\u003c/p\u003e","description":"","filename":"Figures7.png","url":"https://assets-eu.researchsquare.com/files/rs-4007462/v1/2098474709243a78f2e06ffb.png"},{"id":52101389,"identity":"e3e500eb-d326-4b43-b62c-75187f5acb46","added_by":"auto","created_at":"2024-03-06 19:04:42","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":625023,"visible":true,"origin":"","legend":"\u003cp\u003eThe spatial (a) historical, (b) projected, and (c) temporal changes in heavy rainfall (HR) days for SSP2-4.5 and SSP5-8.5 in the near, mid, and far futures.\u003c/p\u003e","description":"","filename":"Figures8.png","url":"https://assets-eu.researchsquare.com/files/rs-4007462/v1/69b0787afdd66e33feafe4f0.png"},{"id":70965302,"identity":"9ea9400a-b325-4a69-afd7-ffd51592090b","added_by":"auto","created_at":"2024-12-09 16:18:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6152472,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4007462/v1/d51db776-044f-461d-9bfb-4880a18d5edf.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessing the Impacts of Future Climate Extremes on Boro Rice Cultivation in the Northeastern Haor Region of Bangladesh: Insights from CMIP6 Multi-Model Ensemble Projections","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe average temperature of the Earth's surface has risen by 1.09\u0026deg;C (0.95\u0026ndash;1.20\u0026deg;C) from 2011 to 2020 compared to the period from 1850\u0026ndash;1900 (IPCC \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This temperature rise has resulted in a notable increase in the frequency and severity of extreme climatic occurrences such as floods, droughts, and heatwaves (IPCC \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Bai et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Islam et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Climate change (CC) has significantly impacted phenology, agricultural yields, water utilization, and public health (He et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Chandarak et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Each 1\u0026deg;C rise in the mean temperature throughout the rice farming season resulted in a 6.2% decrease in the rice yield (Lyman et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Therefore, comprehending and projecting the spatiotemporal alterations in extreme climatic conditions is of utmost importance for establishing adaptation plans to mitigate climate-related risks.\u003c/p\u003e \u003cp\u003eRice is a vital staple food for people worldwide, nourishing over 50% of the world's population (He et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This essential crop not only sustains a primary source of income for farmers across Asia and South America and progressively in Africa but also plays a pivotal role in ensuring food security worldwide (He et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Kumar et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Chandarak et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Bangladesh is positioned as the fourth largest country globally in terms of both rice land area and yields (Uddin and Dhar \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Rice cultivation primarily occurs in subtropical climates, making it susceptible to frequent exposure to extremely high temperatures. Thermal stress can significantly decrease crop productivity, especially during the flowering or reproductive stages (Luo \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Gourdji et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In the reproductive stage, temperatures over 30\u0026deg;C might impact panicle differentiation (PD). In contrast, temperatures ranging from 33 to 35\u0026deg;C during anthesis can lead to the sterility of florets (Matsui and Hasegawa \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). On the other hand, Satake (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e1976\u003c/span\u003e) reported that cold-sensitive varieties experience cold damage at the reproductive stage when the air temperature drops below 20\u0026deg;C, whereas cold-tolerant varieties are affected at 15\u0026deg;C. Crop damage, particularly in terms of spikelet sterility, occurs if the air temperature remains below the critical low temperature for three consecutive days during the reproductive stage. However, the severity of the damage increases significantly if the low temperature persists for more than 5\u0026ndash;6 days (Rashid and Yasmeen \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). As temperatures continue to rise in future Bangladesh (Islam et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kamruzzaman et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023c\u003c/span\u003e), heat stress has emerged as a foremost concern for rice yield (Das et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; IPCC \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; S\u0026aacute;nchez et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Chaturvedi et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGlobal climate models (GCMs) serve as the primary technique for comprehending the possible consequences of CC. The Coupled Model Intercomparison Project phase 6 (CMIP6) GCMs represent an advancement over previous CMIPs in several aspects, such as enhanced geographical resolution, reduced systematic model biases and uncertainties, and improved simulation of cloud microphysical dynamics (Eyring et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Kamruzzaman et al., 2023). Nevertheless, the output data generated by GCMs cannot be readily applied to assess the precise effects of CCs on crops at individual sites because of the coarse resolution of GCMs (Wang et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Islam et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Downscaling techniques are essential for acquiring high-resolution regional- or local-scale climatic data. Dynamical downscaling or statistical downscaling are the primary methods employed for climatic downscaling. Statistical downscaling is often favored because of its simplicity, affordability, fast calculations, and reduced computational demands (Rashid et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Islam et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral studies have concentrated on the influence of extreme climatic indices, or CCs, on rice yield, primarily based on observed data (Huang et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Abbas et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Maniruzzaman et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Vogel et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Rehmani et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Fan et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Song et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), heat stress damage or response mechanisms (Jagadish et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Das et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Shi et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Lawas et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Jagadish et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) conducted greenhouse investigations with different rice genotypes. They found that temperature exposure\u0026thinsp;\u0026gt;\u0026thinsp;33.7\u0026deg;C at anthesis for \u0026lt;\u0026thinsp;1 h was enough to cause sterility. Nevertheless, limited studies have explored the projected impacts of extreme climatic indices on rice production worldwide (He et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Shiru et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). To our knowledge, no studies have been conducted in Bangladesh to date. Furthermore, the future risk of climate extremes is unpredictable due to the uncertainty introduced by various GCMs and multimodel ensemble (MME) approaches. Thus, assessing the future extreme climate risk for rice using the MME of various GCMs is essential.\u003c/p\u003e \u003cp\u003eThe northeastern region of Bangladesh, especially in the \u003cem\u003ehaor\u003c/em\u003e basin, is the primary rice cultivation zone, accounting for approximately 18% of the total rice production of the country (Baishakhy et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; BBS, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).. The northeastern region's distinctive geographical features and water systems have resulted in various livelihood choices and extensive agricultural yields (Nowreen et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Kamruzzaman and Shaw 2018). Approximately 85% of the \u003cem\u003ehaor\u003c/em\u003e basin lands are predominantly dedicated to \u003cem\u003eboro\u003c/em\u003e rice cultivation during the dry period, with the remaining 15% designated for rabi crops (Baishakhy et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Boro rice faces multiple challenges in northeastern regions of Bangladesh, e.g., low-temperature stress at reproductive stages if sowing early, but it is partially safe from flash floods during harvesting; high temperature stress at reproductive stress and flash floods during the maturity stage are challenges for optimum sowing windows (Rashid and Yasmeen \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). \u003cem\u003eBoro\u003c/em\u003e crops in the northeastern areas generally reach maturity by the last week of April, coinciding with the usual occurrence of flash floods between mid-April and May in the same area (Ahmed et al., 2017; Roy et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Due to its heavy reliance on natural conditions, \u003cem\u003eboro\u003c/em\u003e rice cultivation is consistently vulnerable to total damage caused by extreme climatic conditions (Nowreen et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Hence, projecting extreme climate indices is crucial for the northeastern \u003cem\u003ehaor\u003c/em\u003e basin to assess the future climate risk that can hinder food security. This is the pioneering study in Bangladesh, conducted over the northeast region, to project the future extreme climatic stress on rice cultivation.\u003c/p\u003e \u003cp\u003eThis work analyses extreme climatic stress that mainly impacts \u003cem\u003eboro\u003c/em\u003e rice in the northeastern region. The analysis is focused on using statistically downscaled daily climate data from 15 CMIP6 GCMs and employing the MME technique to ensemble the extreme climatic stress indices derived from the GCM outputs. The primary objectives are (i) to investigate the future trends and potential spatial and temporal shifts in future extreme climatic events for the northeastern part of Bangladesh and (ii) to assess their possible impacts on dry season rice (\u003cem\u003eBoro\u003c/em\u003e) production in the northeastern part of Bangladesh.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study area\u003c/h2\u003e \u003cp\u003eThe northeastern region of Bangladesh, characterized by its distinct hydroecological attributes, consists of extensive bowl-shaped floodplain depressions within the Meghna River basin in the northeast part of Bangladesh (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The northeastern haor basin's main portion encompasses four Bangladesh districts: Sunamganj, Sylhet, Habiganj, and Moulavibazar. Additionally, it extends to the Netrokona, Brahmanbaria, and Kishoreganj districts, which lie beyond the major region (Roy et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Encompassing an expansive 1.99\u0026nbsp;million hectares (19,998 \u003csup\u003ekm2\u003c/sup\u003e) of land, this region serves as a habitat for an estimated population of approximately 19.37\u0026nbsp;million. According to records, approximately 373 \u003cem\u003ehaor\u003c/em\u003e or wetland areas, approximately 859,000 hectares, account for approximately 43% of the entire land area within the northeastern districts (CEGISCEGIS, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). This area has unique hydrological attributes. The annual precipitation exhibits a variation in the range of 2,200 mm near the western periphery and 5,800 mm in the northeastern extremity of the region. In certain catchments that reach India, the headwaters can receive as much as 12,000 mm of precipitation. The primary reason for flash floods in the northeastern region is the water flow from India to Bangladesh (CEGISCEGIS, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The rainfall during the winter season in the northeast region is visible due to the influence of CC. The occurrence of dense fog is a common scene during this period. The mean annual maximum and minimum temperatures are 33.30\u0026deg;C and 12.00\u0026deg;C, respectively. The dry season is hot and clear, whereas the rainy season is muggy, uncomfortable, and overcast. The soil in this region mostly consists of alluvial sand, which originates from the Brahmaputra, Meghna, and other interconnected smaller rivers (Haque et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). It has a prolonged dry period lasting approximately six months, followed by a subsequent period of submersion for the remaining months of the year. The cultivable land in this area is utilized throughout the winter to produce boro rice. During the monsoon season, the same region transforms into a breeding ground for open-water fisheries, facilitating the growth of diverse biological species (Roy et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data sources\u003c/h2\u003e \u003cp\u003eThe weather stations in Bangladesh are unevenly scattered across the country, and there is a limited presence of meteorological observation stations in remote and high-altitude mountainous regions. There are only two meteorological stations in the northeastern region, so ERA5 reanalysis datasets were used to provide gridded climate variable data for this region. Studies have found that ERA5 demonstrated outstanding performance in Bangladesh, as assessed by the different criteria employed for precipitation and temperature (Islam and Cartwright \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kamruzzaman et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This work employed ERA5 datasets (website download link: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5\u003c/span\u003e\u003cspan address=\"https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) with a geographical resolution of 0.25\u0026deg;, covering the period from 1995\u0026ndash;2014.\u003c/p\u003e \u003cp\u003eThis research assessed spatiotemporal changes in extreme climatic indices in three future periods: near (2021\u0026ndash;2040), mid (2041\u0026ndash;2060), and far (2061\u0026ndash;2100), utilizing 15 CMIP6 GCMs (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) for two SSPs: SSP2-4.5 and SSP5-8.5. The data were accessed from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://esgf-node.llnl.gov/search/cmip6/\u003c/span\u003e\u003cspan address=\"https://esgf-node.llnl.gov/search/cmip6/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The SSP2-4.5 scenario is moderate, projecting a 2.7\u0026deg;C increase in the global average temperature by 2100. In contrast, SSP5-8.5 represents the most significant alteration.\u003c/p\u003e \u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eList of the global climate models (GCMs) utilized in this study.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.224489795918368%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSerial\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.04081632653061%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eInstitution\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eResolution\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003elongitude\u0026nbsp;\u0026times; latitude\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.224489795918368%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003eACCESS-CM2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.04081632653061%\" valign=\"top\"\u003e\n \u003cp\u003eAustralian Community Climate and Earth-System Simulator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\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=\"11.224489795918368%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003eACCESS-ESM1-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.04081632653061%\" valign=\"top\"\u003e\n \u003cp\u003eAustralian Community Climate and Earth-System Simulator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\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=\"11.224489795918368%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003eCanESM5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.04081632653061%\" valign=\"top\"\u003e\n \u003cp\u003eCanadian Earth System Model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e2.81\u0026deg; \u0026times; 2.79\u0026deg;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.224489795918368%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003eCNRM-CM6-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.04081632653061%\" valign=\"top\"\u003e\n \u003cp\u003eNational Centre for Meteorological Research, France\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e1.41\u0026deg; \u0026times; 1.40\u0026deg;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.224489795918368%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003eCNRM-ESM2-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.04081632653061%\" valign=\"top\"\u003e\n \u003cp\u003eNational Centre for Meteorological Research, France\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e1.41\u0026deg;\u0026times;1.40\u0026deg;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.224489795918368%\" valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003eGFDL-ESM4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.04081632653061%\" valign=\"top\"\u003e\n \u003cp\u003eNOAA/ Geophysical Fluid Dynamics Laboratory, USA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e1.30\u0026deg;\u0026times;1.00\u0026deg;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.224489795918368%\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003eINM-CM4-8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.04081632653061%\" valign=\"top\"\u003e\n \u003cp\u003eInstitute for Numerical Mathematics, Russia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e2.00\u0026deg; \u0026times; 1.50\u0026deg;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.224489795918368%\" valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003eINM-CM5-0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.04081632653061%\" valign=\"top\"\u003e\n \u003cp\u003eInstitute for Numerical Mathematics, Russia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e2.00\u0026deg; \u0026times; 1.50\u0026deg;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.224489795918368%\" valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003eIPSL-CM6A-LR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.04081632653061%\" valign=\"top\"\u003e\n \u003cp\u003eInstitut Pierre Simon Laplace, France\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e2.50\u0026deg; \u0026times; 1.26\u0026deg;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.224489795918368%\" valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003eMIROC6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.04081632653061%\" valign=\"top\"\u003e\n \u003cp\u003eAtmosphere and Ocean Research Institute (The University of Tokyo), National Institute for Environmental Studies, and Japan Agency for Marine-Earth Science and Technology, Japan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e1.41\u0026deg; \u0026times; 1.40\u0026deg;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.224489795918368%\" valign=\"top\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003eMPI-ESM1-2-LR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.04081632653061%\" valign=\"top\"\u003e\n \u003cp\u003eMax Planck Institute for Meteorology, Germany\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e1.88\u0026deg; \u0026times; 1.86\u0026deg;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.224489795918368%\" valign=\"top\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003eMPI-ESM1-2-HR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.04081632653061%\" valign=\"top\"\u003e\n \u003cp\u003eMax Planck Institute for Meteorology, Germany\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e1.88\u0026deg; \u0026times; 1.86\u0026deg;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.224489795918368%\" valign=\"top\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003eMRI-ESM2-0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.04081632653061%\" valign=\"top\"\u003e\n \u003cp\u003eMeteorological Research Institute, Japan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e1.13\u0026deg; \u0026times; 1.12\u0026deg;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.224489795918368%\" valign=\"top\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003eNorESM2-LM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.04081632653061%\" valign=\"top\"\u003e\n \u003cp\u003eNorwegian Climate Center, Norway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e2.50\u0026deg; \u0026times; 1.89\u0026deg;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.224489795918368%\" valign=\"top\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003eUKESM1-0-LL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.04081632653061%\" valign=\"top\"\u003e\n \u003cp\u003eUK Met Office Hadley Office, UK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"top\"\u003e\n \u003cp\u003e1.88\u0026deg;\u0026nbsp;\u0026times; 1.25\u0026deg;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Climate Extreme Indices\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e provides a comprehensive overview of the extreme climate indices investigated in this research. Our study focuses on five crucial extreme climatic stress indices: three consecutive cold days (CCD3), six consecutive cold days (CCD6), hot days (HD), consecutive hot days (CHD), and heavy rainfall (HR) days. These indices were selected to evaluate their potential impact on \u003cem\u003eboro\u003c/em\u003e rice production in northeastern Bangladesh, particularly during the critical reproductive stage, as shown in the generic crop calendar in the northeast region of Bangladesh (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDefinitions of five extreme climate indices were used in this study.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026times;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerial\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInstitution\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eResolution\u003c/p\u003e \u003cp\u003elongitude \u0026times; latitude\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACCESS-CM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAustralian Community Climate and Earth-System Simulator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cp\u003e1.88\u0026deg; \u0026times; 1.25\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACCESS-ESM1-5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAustralian Community Climate and Earth-System Simulator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cp\u003e1.88\u0026deg; \u0026times; 1.25\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCanESM5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCanadian Earth System Model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cp\u003e2.81\u0026deg; \u0026times; 2.79\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCNRM-CM6-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNational Centre for Meteorological Research, France\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cp\u003e1.41\u0026deg; \u0026times; 1.40\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCNRM-ESM2-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNational Centre for Meteorological Research, France\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cp\u003e1.41\u0026deg;\u0026times;1.40\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGFDL-ESM4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNOAA/ Geophysical Fluid Dynamics Laboratory, USA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cp\u003e1.30\u0026deg;\u0026times;1.00\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eINM-CM4-8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInstitute for Numerical Mathematics, Russia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cp\u003e2.00\u0026deg; \u0026times; 1.50\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eINM-CM5-0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInstitute for Numerical Mathematics, Russia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cp\u003e2.00\u0026deg; \u0026times; 1.50\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIPSL-CM6A-LR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInstitut Pierre Simon Laplace, France\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cp\u003e2.50\u0026deg; \u0026times; 1.26\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMIROC6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAtmosphere and Ocean Research Institute (The University of Tokyo), National Institute for Environmental Studies, and Japan Agency for Marine-Earth Science and Technology, Japan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cp\u003e1.41\u0026deg; \u0026times; 1.40\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMPI-ESM1-2-LR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMax Planck Institute for Meteorology, Germany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cp\u003e1.88\u0026deg; \u0026times; 1.86\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMPI-ESM1-2-HR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMax Planck Institute for Meteorology, Germany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cp\u003e1.88\u0026deg; \u0026times; 1.86\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMRI-ESM2-0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMeteorological Research Institute, Japan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cp\u003e1.13\u0026deg; \u0026times; 1.12\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNorESM2-LM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNorwegian Climate Center, Norway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cp\u003e2.50\u0026deg; \u0026times; 1.89\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUKESM1-0-LL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUK Met Office Hadley Office, UK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cp\u003e1.88\u0026deg; \u0026times; 1.25\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIt is essential to highlight that heavy rainfall can result in flooding, which, in turn, significantly affects rice production. Flooding during harvesting substantially damages crops (Kamruzzaman et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003eb\u003c/span\u003e). Moreover, high temperatures exceeding 35\u0026deg;C can have detrimental effects on rice production, especially during the flowering and growth stages, leading to reduced rice yields (Das et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Conversely, critically low temperatures during the reproductive phase can also harm rice production (Kabir et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Methodologies\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1 Downscaling and bias correction\u003c/h2\u003e \u003cp\u003eThe raw GCM simulations obtained from data extraction were first interpolated to match observed locations. Subsequently, simple quantile mapping (SQM) was utilized to fix the bias in the distribution of GCM simulations, aligning them with the distribution of observed ERA5 data at each respective site. It is worth noting that the nonparametric empirical formula employed in quantile mapping (QM) proves more skillful at reducing systematic bias compared to parametric approaches (Gudmundsson et al. 2012). The bias-corrected GCM data generated by QM closely align with the observed distribution, significantly bolstering the dependability of climate predictions (Heo et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). As a result, this approach has gained widespread adoption for downscaling GCM simulations (Pierce et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Alamgir et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this research, daily precipitation, maximum temperature (Tmax), and minimum temperature (Tmin) data for the years spanning from 1995\u0026ndash;2100 were downscaled to a standard resolution (0.25) by utilizing the SQM method. Subsequently, bias correction was performed based on ERA5 grids, employing the rSQM software developed by Cho et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This study utilized a three-step process for bias correction. First, each target location's global climate model (GCM) data were obtained. Second, an assessment of the inherent biases within the GCM data was conducted. Finally, bias correction methods were applied to the projections. Differences in cumulative distribution functions (CDFs) between observed and simulated data for the retrospective period were calculated and subsequently used to adjust future simulations for a specified percentile, as detailed in Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$${x}_{p}^{{\\prime }}\\left(t\\right)={x}_{p}\\left(t\\right)+{F}_{obs}^{-1}\\left({F}_{p.sim}\\right({x}_{p}\\left(t\\right))-{F}_{r.sim}^{-1}({F}_{p.sim}\\left({x}_{p}\\left(t\\right)\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn this context, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(F\\left(\\theta \\right)\\)\u003c/span\u003e\u003c/span\u003eand \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({F}^{-1}\\left(\\theta \\right)\\)\u003c/span\u003e\u003c/span\u003e represent the CDF and its inverse for the daily data θ, respectively, while \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({x}_{p}^{{\\prime }}\\left(t\\right)\\)\u003c/span\u003e\u003c/span\u003eand \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({x}_{p}\\left(t\\right)\\)\u003c/span\u003e\u003c/span\u003e signify the bias-corrected and raw future projections on day t. The subscripts \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(p.sim\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(r.sim\\)\u003c/span\u003e\u003c/span\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(obs\\)\u003c/span\u003e\u003c/span\u003e denote the future prediction, retrospective simulation, and daily observed data, respectively (Kamruzzaman et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2 Multimodel ensemble (MME) approach\u003c/h2\u003e \u003cp\u003eThe extreme climate indices were computed using 15 different downscaled CMIP6 GCM rainfall, Tmax, and Tmin values over the historical period spanning from 1995 to 2014. The inherent uncertainty in climate modeling is a fundamental characteristic resulting from the intricate nature of the Earth's climate system and the constraints of scientific comprehension and data availability. This work constructed an MME by finding the average of the projections from 15 CMIP6 GCMs for both the historical and future. This was done to reduce the projections' uncertainty and better understand how each index has changed. The calculation of the MME was conducted for three future periods: near (2021\u0026ndash;2040), mid (2041\u0026ndash;2060), and far (2061\u0026ndash;2100). To calculate changes, the study compared the anticipated MME to the historical downscaled MME for future periods. This work also examined the spatiotemporal aspects of these alterations to determine locations more susceptible to the consequences of CCs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.4.3 Modified Mann\u0026ndash;Kendall (MMK) test\u003c/h2\u003e \u003cp\u003eTrend analysis is an essential technique for comprehending alterations in hydroclimatic datasets. The present study employed the modified Mann\u0026ndash;Kendall (MMK) test proposed by Hamed and Ramachandra Rao (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1998\u003c/span\u003e), which considers autocorrelation when calculating variance for climatic datasets. The following equation is employed to calculate autocorrelation in the data:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$${V}^{*}\\left(S\\right)=var\\left(S\\right).\\frac{n}{{n}_{s}^{*}}=\\frac{n(n-1)(2n+5)}{18}.\\frac{n}{{n}_{s}^{*}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003en/\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({n}_{s}^{*}\\)\u003c/span\u003e\u003c/span\u003e denotes an adjustment caused by autocorrelation in the data. The empirical formula offers the most precise estimation of the theoretical values derived from \u003cem\u003en/\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({n}_{s}^{*}\\)\u003c/span\u003e\u003c/span\u003e expressed as follows:\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\frac{n}{{n}_{s}^{*}}=1+\\frac{2}{n(n-1)(n-2)}\\times \\sum _{i=1}^{n-1}(n-i)(n-i-1)(n-i-2){\\rho }_{s}\\left(i\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003en\u003c/em\u003e represents the actual observation number and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\rho }_{s}\\left(i\\right)\\)\u003c/span\u003e\u003c/span\u003e is the autocorrelation function of the observation rankings. The approximation facilitates evaluating the variance of S, considering the observation rankings without using data normalization or autocorrelation functions (Hamed and Rao \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1998\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Bias-corrected Outputs Evaluation\u003c/h2\u003e \u003cp\u003eThe comparison of downscaled precipitation, Tmax, and Tmin of the ensemble mean of 15 CMIP6 GCMs is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The data were evaluated before and after bias correction and assessed with observed values. The ensemble mean raw GCMs exhibited considerable dry biases across the year except for the winter (DJF) months in mean monthly rainfall. Moreover, the analysis revealed the presence of significant warm biases in Tmax and Tmin, particularly during the premonsoon (MAM) and monsoon (JJAS) seasons. The raw ensemble mean tended to underestimate the observed rainfall during the months of MAM and JJAS by approximately 54.33\u0026ndash;387.24 and 98.97\u0026ndash;398.68 mm/month, respectively.\u003c/p\u003e \u003cp\u003eConversely, the ensemble mean overestimated the rainfall from December to January by approximately 7.91\u0026ndash;17.01 mm/month. The raw simulated Tmax overestimated the MAM and JJAS months by approximately 1.66\u0026deg;C\u0026ndash;4.18\u0026deg;C and 0.94\u0026deg;C\u0026ndash;3.75\u0026deg;C, respectively, whereas November to January was underestimated by approximately 0.48\u0026deg;C\u0026ndash;1.48\u0026deg;C. Additionally, the raw simulated Tmin tended to underestimate the observed Tmin from April to September by a range of 0.80\u0026deg;C\u0026ndash;2.13\u0026deg;C. The approach of bias correction aims to reduce these errors. The present work utilized an SQM technique for bias correction. This correction led to a more pronounced alignment between the monthly average data and the observed values. After applying the SQM bias correction technique, a significant improvement was noticed for rainfall, where R\u003csup\u003e2\u003c/sup\u003e increased from 0.68 to the perfect 1. Similarly, bias was reduced for Tmax and Tmin and improved R\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Trends in extreme climate indices\u003c/h2\u003e \u003cp\u003eThe MMK trends of severe climate indicators for historical and projected SSP2-4.5 and SSP5-8.5 scenarios over Bangladesh from 2020\u0026ndash;2100 are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. For the historical period, only CCD3 exhibited a significant decreasing trend, while all other extreme climatic indices showed an insignificant decreasing trend except for HR days.\u003c/p\u003e \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAssess Modified Mann-Kendall (MMK) trends for extreme climate indices during the historical period (1995\u0026ndash;2014) and the projected period (2020\u0026ndash;2100).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\" style=\"width: 9.4126%;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" style=\"width: 25.4803%;\"\u003e\n \u003cp\u003eHistorical\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" style=\"width: 23.9049%;\"\u003e\n \u003cp\u003eSSP2-4.5\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" style=\"width: 36.2161%;\"\u003e\n \u003cp\u003eSSP5-8.5\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" style=\"width: 14.7925%;\"\u003e\n \u003cp\u003eSS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 10.7262%;\"\u003e\n \u003cp\u003eZc\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 10.7902%;\"\u003e\n \u003cp\u003eSS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 13.1372%;\"\u003e\n \u003cp\u003eZc\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 19.8227%;\"\u003e\n \u003cp\u003eSS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 16.7712%;\"\u003e\n \u003cp\u003eZc\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 9.4126%;\"\u003e\n \u003cp\u003eCCD3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 14.7925%;\"\u003e\n \u003cp\u003e\u0026ndash;0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 10.7262%;\"\u003e\n \u003cp\u003e\u0026ndash;2.37\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 10.7902%;\"\u003e\n \u003cp\u003e\u0026ndash;0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 13.1372%;\"\u003e\n \u003cp\u003e\u0026ndash;6.03\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 19.8227%;\"\u003e\n \u003cp\u003e\u0026ndash;0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 16.7712%;\"\u003e\n \u003cp\u003e\u0026ndash;4.83\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 9.4126%;\"\u003e\n \u003cp\u003eCCD6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 14.7925%;\"\u003e\n \u003cp\u003e\u0026ndash;0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 10.7262%;\"\u003e\n \u003cp\u003e\u0026ndash;1.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 10.7902%;\"\u003e\n \u003cp\u003e\u0026ndash;0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 13.1372%;\"\u003e\n \u003cp\u003e\u0026ndash;6.68\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 19.8227%;\"\u003e\n \u003cp\u003e\u0026ndash;0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 16.7712%;\"\u003e\n \u003cp\u003e\u0026ndash;5.01\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 9.4126%;\"\u003e\n \u003cp\u003eHD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 14.7925%;\"\u003e\n \u003cp\u003e\u0026ndash;0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 10.7262%;\"\u003e\n \u003cp\u003e\u0026ndash;0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 10.7902%;\"\u003e\n \u003cp\u003e0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 13.1372%;\"\u003e\n \u003cp\u003e7.94\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 19.8227%;\"\u003e\n \u003cp\u003e0.241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 16.7712%;\"\u003e\n \u003cp\u003e6.05\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 9.4126%;\"\u003e\n \u003cp\u003eCHD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 14.7925%;\"\u003e\n \u003cp\u003e\u0026ndash;0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 10.7262%;\"\u003e\n \u003cp\u003e\u0026ndash;1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 10.7902%;\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 13.1372%;\"\u003e\n \u003cp\u003e7.80\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 19.8227%;\"\u003e\n \u003cp\u003e0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 16.7712%;\"\u003e\n \u003cp\u003e5.77\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 9.4126%;\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 14.7925%;\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 10.7262%;\"\u003e\n \u003cp\u003e1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 10.7902%;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 13.1372%;\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 19.8227%;\"\u003e\n \u003cp\u003e\u0026ndash;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 16.7712%;\"\u003e\n \u003cp\u003e\u0026ndash;0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 54.4974%;\"\u003e\u003cem\u003eCCD3, three consecutive cold days; CCD6, six consecutive cold days; HD, hot days; CHD, consecutive hot days; HR, heavy rainfall days; SS, Sen\u0026rsquo;s Slope; Zc, Kendall\u0026apos;s test statistics. Asterisks designate significance level where\u003c/em\u003e \u003csup\u003e\u003cem\u003e*\u003c/em\u003e\u003c/sup\u003e \u003cem\u003eindicate p-values of \u0026lt;\u0026thinsp;0.01.\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e \u003cp\u003eThe projected (2020\u0026ndash;2100) period results displayed significant decreasing trends in CCD3 and CCD6 and significantly increasing trends in HD and CHD for SSP2-4.5 and SSP5-8.5. However, no important trends were detected for HR days. The rates of decline in CCD3 were observed to be 0.30 and 0.35 days/decade for SSP2-4.5 and SSP5-8.5, respectively. In the case of CCD6, the decreasing rates were 0.23 and 0.24 days/decade for SSP2-4.5 and SSP5-8.5, respectively. The increase in HD was identified at a rate of 1.12 days/decade for SSP2-4.5 and 2.41 days/decade for SSP5-8.5. The rise in CHD stands at 0.61 days/decade and 1.36 days/decade for SSP2-4.5 and SSP5-8.5, respectively. Both scenarios detected an insignificant rise in HR at 0.01 days/decade.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Spatiotemporal changes in extreme indices\u003c/h2\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1 Cold stress indices\u003c/h2\u003e \u003cdiv id=\"Sec15\" class=\"Section4\"\u003e \u003ch2\u003e3.3.1.1 Three Consecutive Cold Days\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e depicts the historical and predicted variation in the occurrence of three consecutive cold days (CCD3) in the northeastern region, considering various scenarios and future timeframes compared to the reference period from 1995 to 2014. During the historical period, the total CCD3 ranged between 0.76 and 9.94 days over the study area. The historical CCD3 was greater in the northeastern region and lower toward the southern part (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe study demonstrates that across all projected periods and SSPs, there is a noticeable negative shift in CCD3. There is a notable decrease (up to approximately \u0026minus;\u0026thinsp;9 days for SSP5-8.5) in CCD3 in both scenarios in the northeastern region of the northeast \u003cem\u003ehaor\u003c/em\u003e basin, including the Sylhet, Sunamgonj, and partly Netrakona districts. On the other hand, a relatively lower projected decrease in CCD3 was noticed in the southern region (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). For SSP2-4.5, the MME estimates a decrease in CCD3 of \u0026minus;\u0026thinsp;0.84 days (\u0026ndash;0.30 to \u0026minus;\u0026thinsp;1.72) in the near future, \u0026minus;\u0026thinsp;1.71 days (\u0026ndash;0.52 to \u0026minus;\u0026thinsp;4.28) in the mid-future, and \u0026minus;\u0026thinsp;2.52 days (\u0026ndash;0.72 to \u0026minus;\u0026thinsp;6.59 days) in the far future. Similarly, for SSP5-8.5, the estimated changes in CCD3 range from \u0026minus;\u0026thinsp;0.77 days (\u0026ndash;0.28 to \u0026minus;\u0026thinsp;1.65 days) in the near future, \u0026minus;\u0026thinsp;2.09 days (\u0026ndash;0.55 to \u0026minus;\u0026thinsp;5.17 days) in the mid-future, and \u0026minus;\u0026thinsp;3.06 days (\u0026ndash;0.75 to \u0026minus;\u0026thinsp;8.78 days) in the far future. The projected CCD3 changes exhibited a greater declining pattern in the far future than in the near and mid future (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section4\"\u003e \u003ch2\u003e3.3.1.2 Six consecutive cold days\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e illustrates the historical and projected spatiotemporal changes in Six Consecutive Cold Days (CCD6) for the near, mid and far futures under the scenarios SSP2-4.5 and SSP5-8.5. The estimated historical (1995\u0026ndash;2014) CCD6 throughout the northeastern region exhibited a range of 0.76 days to 9.94 days. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea displays the historical concentration of CCD6, revealing a notable spike in the northeast region, which weakens as one moves toward the central, northwestern, and southern regions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSimilar to CCD3, CCD6 also displays an apparent decline in all anticipated periods and SSPs over the northeastern region. The region that might experience the most significant decrease (up to approximately \u0026minus;\u0026thinsp;9 days for SSP5-8.5) in CCD6 was identified as the northeastern region (Sylhet and Sunamgonj districts). In contrast, the southern region showed relatively lower increases (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). The CCD6 was projected to decrease by an average of \u0026minus;\u0026thinsp;0.72 days (0 to \u0026minus;\u0026thinsp;1.92 days) in the near period, \u0026minus;\u0026thinsp;1.41 days (\u0026ndash;0.15 to \u0026minus;\u0026thinsp;4.69 days) in the mid period, and \u0026minus;\u0026thinsp;2.04 days (\u0026ndash;0.23 to \u0026minus;\u0026thinsp;7.09 days) in the far period for SSP2-4.5. Likewise, for SSP5-8.5, the projected changes in CCD6 are an average of \u0026minus;\u0026thinsp;0.63 days (\u0026ndash;0.08 to \u0026minus;\u0026thinsp;1.81 days) in the near period, \u0026minus;\u0026thinsp;1.69 days (\u0026ndash;0.13 to \u0026minus;\u0026thinsp;5.74 days) in the mid period, and \u0026minus;\u0026thinsp;2.37 days (\u0026ndash;0.24 to \u0026minus;\u0026thinsp;8.73 days) in the far period. This result revealed that anticipated changes in CCD6 projections demonstrate a more pronounced decline in the far future relative to the near and mid futures, which are identical to the CCD3 changes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2 Heat stress indices\u003c/h2\u003e \u003cdiv id=\"Sec18\" class=\"Section4\"\u003e \u003ch2\u003e3.3.2.1 Hot days\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e depicts the historical and projected spatiotemporal changes in hot days (HD) for the near, mid, and far futures under the SSP2-4.5 and SSP5-8.5 scenarios in the northeastern region. During the historical period, the HD frequency ranged from 0\u0026ndash;5.91 days over the study area. A higher HD was observed in the southwestern region (Kishoreganj and Brahmanbaria districts), while it was relatively lower in the northeastern region (Sylhet and Sunamgonj districts) of the study area (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). The HD increased in all periods and scenarios. For both scenarios, a noticeable increase in HD was observed in the southwestern part of the northeastern region (Kishoreganj and Brahmanbaria districts). In contrast, the northeast region (Sylhet and Sunamgonj districts) experienced relatively lower rises (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). In the context of SSP2-4.5, the projected increase in HD is estimated to be an average of 0.61 days (0\u0026ndash;1.17 days) in the near future, 2.44 days (0.02\u0026ndash;4.42 days) in the mid-future, and 6.26 days (0.37\u0026ndash;10.40 days) in the far future. Similarly, for SSP5-8.5, the estimated changes in HD are an average of 0.62 days (0.02\u0026ndash;1.18 days) in the near future, 3.95 days (0.11\u0026ndash;6.93 days) in the mid-future, and 12.04 days (1.40\u0026ndash;17.80 days) in the far future. The projected HD changes revealed a greater increasing pattern in the far future than in the near and mid future (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section4\"\u003e \u003ch2\u003e3.3.1.2 Consecutive hot days (CHD)\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e exhibits the historical and predicted spatial and temporal changes in consecutive hot days (CHDs) all over the northeastern region for the near, mid, and far future periods for the SSP2-4.5 and SSP5-8.5 scenarios. Throughout the historical era, the frequency of CHD varied between 0 and 3.08 days. The study area had a greater CHD in the southwestern region, namely, in the districts of Kishoreganj and Brahmanbaria. Conversely, the northeastern region, encompassing the districts of Sylhet and Sunamgonj, displayed a comparatively lower CHD (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea). This study expects CHD to increase in both periods and scenarios. For both scenarios, a notable rise in HD was observed in the southwestern (Kishoreganj and Brahmanbaria districts) area of the northeastern \u003cem\u003ehaor\u003c/em\u003e basin. In contrast, the northeastern part (Sylhet and Sunamgonj districts) showed relatively lower increases (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb). The CHD was projected to increase by an average of 0.38 days (0\u0026ndash;0.83 days) in the near period, 1.31 days (0\u0026ndash;2.46 days) in the mid period, and 3.52 days (0.19\u0026ndash;6.02 days) in the far period for SSP2-4.5. Similarly, the expected increases in CHD for SSP5-8.5 were an average of 0.34 days (0.01\u0026ndash;0.78 days) in the near period, 2.15 days (0.06\u0026ndash;3.76 days) in the mid period, and 6.85 days (0.72\u0026ndash;10.48 days) in the far period. The results of this study demonstrate that anticipated changes in CHD projections exhibit a more noticeable increase in the far future than in the near and mid futures, which aligns with HD changes (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e3.3.3 Heavy rainfall days\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e presents the historical projected changes in heavy rainfall (HR) days over the northeastern region under different scenarios and future periods. The estimated historical HR over the northeast region ranged from 1.70 to 2.88 days. The higher HR is observed mostly in the Sylhet and partly in the Sunamgonj and Habiganj districts in our study area's central and northeastern parts. In contrast, a lower HR is observed in the southern and western regions in the study area (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea). For all periods and scenarios, the anticipated HR showed a minimal (up to \u0026minus;\u0026thinsp;0.20 to 0.30 days) change with a mix of increasing and decreasing patterns, indicating that climate change would probably have little impact on the HR in the study area. An increase in HR was noticed in the northeastern region, while there was a decrease in the western region for both SSPs in the near future. In the mid-future, the central to southern regions showed a higher increase for SSP2-4.5. However, it was narrowed to the central region for SSP5-8.5. A higher increase in HR was noticed in the northeastern region for SSP2-4.5, while it was shifted to the rest of the regions for SSP5-8.5 for the far future (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eb). The projected mean changes for SSP2-4.5 in the near, mid, and far periods were \u0026minus;\u0026thinsp;0.02, \u0026minus;\u0026thinsp;0.08, and \u0026minus;\u0026thinsp;0.01 days, respectively, while those for SSP5-8.5 were 0.09, 0.05, and 0.11, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Discussions","content":"\u003cp\u003eTemperature is a significant environmental factor that plays a crucial role in influencing plant growth, development, and yield. Global warming poses a severe threat to the productivity of crops on a global scale by prolonged exposure to temperatures above the optimal range for plant growth (Janni et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Xu et al. \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The present study examined future trends and anticipated alterations in five extreme thermal stress (CCD3, CCD6, HD, CHD, and HR) indices. These findings were based on MME averages derived from 15 GCM simulations of rainfall, Tmax, and Tmin under the CMIP6 framework for both medium (SSP2-4.5) and high (SSP5-8.5) scenarios. Indicators linked to heat stress are deemed more definite and immediate because of their direct association with temperature. These indicators significantly influence agricultural productivity, mainly rice, especially in the northeastern regions of Bangladesh, such as the \u003cem\u003eHaor B\u003c/em\u003easin. This area is heavily dependent on rice production and might be particularly susceptible to changes in extreme climatic indices. Evaluating these alterations can provide a precise understanding of extreme climatic conditions in a specific environment, facilitating a deeper comprehension of potential consequences and adaptation prospects.\u003c/p\u003e \u003cp\u003eWe found that there was a noteworthy increasing trend in extreme heat stress (HD and CHD) indices and a noteworthy decreasing trend in cold stress (CCD3 and CCD6) indices over the northeastern \u003cem\u003ehaor\u003c/em\u003e basin from 2020\u0026ndash;2100 for both the SSP2-4.5 and SSP5-8.5 scenarios. The study area had no significant trend in the projected HR days index. A similar representation was noticed in projected changes in extreme climatic stress indices for all periods and scenarios. This situation suggests an overall warming in the future northeastern \u003cem\u003ehaor\u003c/em\u003e basin climate, as expected, as shown in previous studies by the MME of CMIP5 (Alamgir et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Islam et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and CMIP6 (Kamruzzaman et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023c\u003c/span\u003e) GCMs over the entirety of Bangladesh. Rice plants are more vulnerable to heat stress during the reproductive stage, which includes panicle initiation, flowering, development of male and female gametophytes, anthesis including pollination, and fertilization, compared to the vegetative stage (Arshad et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Jagadish et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Xu et al. \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The reproductive phase of dry season rice cultivation in the northeastern region of Bangladesh primarily occurs in mid-March and April, overlapping with the optimum temperatures (38\u0026deg;C \u0026minus;\u0026thinsp;41\u0026deg;C) observed throughout the year (Siddik and Rahman \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMoreover, the projected heat stress increases in this time would increase the temperature in the northeastern region, which might exacerbate adverse impacts. Heat stress hinders the process of panicle initiation and the development of spikelets, resulting in the deformation of floral organs and a decrease in both the size and number of spikelets in rice (Cao et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Xu et al. \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), which might impact the \u003cem\u003ehoar\u003c/em\u003e region of rice plants in the future. Elevated temperatures hinder the process of anther development, resulting in a decrease in the functionality and germination of pollen (Tang et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), which might be attributed to the inhibited growth of pollen mother cells, unusual breakdown of the tapetum, inadequate nutrient accumulation, and restricted sugar movement to the pollen (Endo et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Rezaul et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur study found a higher increase in HD and CHD in the northeastern region of Bangladesh, which has detrimental impacts on the flowering stage, as it is susceptible to extreme temperature stress (Satake and Yoshida \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e1978\u003c/span\u003e; Yoshida and Nagato \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The period of anthesis in rice is considered crucial when it faces heat stress, where processes including dehiscence of anthers, pollination, germination of pollen, and pollen tube development are influenced within 45 minutes after spikelet opening (Prasad et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Arshad et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Prior research has demonstrated that heat stress negatively affects the fertility of rice spikelets when exposed to temperatures over 35\u0026deg;C for approximately 5 days during the flowering stage at anthesis, resulting in decreased or no yield (Satake and Yoshida \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e1978\u003c/span\u003e; Jagadish et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Elevated temperatures lead to a reduction in the fertility of rice spikelets by diminishing the viability of pollen, hindering the dehiscence of anthers, and obstructing the germination of pollen tubes (Das et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Shrestha et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Wang et al. (\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) found a considerable decrease in spikelet fertility of single-season rice at 38\u0026deg;C compared to 32\u0026deg;C and 35\u0026deg;C for 3 days or more in China.\u003c/p\u003e \u003cp\u003eThe vulnerability of rice to heat stress in the future world with higher temperatures underscores the need to prioritize targeted adaptations or mitigation methods, particularly in the southern to southwestern regions (Brahmanbaria and Kishoreganj) of the northeastern \u003cem\u003ehaor\u003c/em\u003e basin, where heat is projected to become more prominent. Choosing heat-tolerant cultivars can significantly mitigate rice yield reductions resulting from heat stress. Nevertheless, heat-resistant cultivars are still susceptible to heat stress hazards when temperatures exceed their tolerance limit due to temperature instabilities.\u003c/p\u003e \u003cp\u003eNumerous crop management practices, such as agronomic management (changing the planting time of rice, applying growth regulators, and mist spray therapy) (Wu et al. \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Khan et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wu and Yang \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Jiang et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), avoiding heat by initiating flowering in the early morning (Jagadish et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Julia and Dingkuhn \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Hirabayashi et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), enhancing thermotolerance through conventional breeding (Driedonks et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Kilasi et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Raza et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), thermotolerance breeding by discovering heat resistance genes (Wei et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), transgenic strategies (Rerksiri et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Shen et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and genome editing techniques (Qiu et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Gao \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), have demonstrated efficacy in mitigating or preventing heat-induced harm in rice. However, modification of the planting schedule is difficult for the northeastern \u003cem\u003ehaor\u003c/em\u003e basin due to the late vacation of land from rainy season water stagnation and the higher risk of floods and flash floods if plating is delayed.\u003c/p\u003e"},{"header":"5. Implication of the findings","content":"\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Practical implications\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eCrop management strategies\u003c/strong\u003e \u003cp\u003eThe insights from the study underscore the immediate need for practical crop management strategies in the northeastern \u003cem\u003ehaor\u003c/em\u003e basin of Bangladesh. Farmers can benefit considerably by adjusting rice planting schedules, opting for heat-tolerant rice varieties, and incorporating agronomic practices that mitigate the impact of escalating heat stress during crucial reproductive stages.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eTechnology adoption for climate resilience\u003c/strong\u003e \u003cp\u003ePractical implications extend to adopting technology-driven solutions, including precision agriculture tools and real-time weather forecasting. These technologies empower farmers to make informed decisions on irrigation, pest control, and other intercultural activities, enhancing the overall resilience of rice cultivation amidst evolving climate conditions.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eGovernment policies and support\u003c/strong\u003e \u003cp\u003ePolicymakers should consider practical interventions such as financial incentives and subsidies to motivate farmers to adopt climate-resilient practices. Investments in research and development for heat-resistant rice varieties and the development of climate-responsive infrastructure are vital elements of government support to fortify agricultural resilience.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEducational programs and extension services\u003c/strong\u003e \u003cp\u003eImplementing extension services and educational programs emerges as a practical avenue to raise awareness among \u003cem\u003ehaor\u003c/em\u003e area farmers. Equipping farmers with the requisite knowledge and skills through educational initiatives empowers them to implement practical measures for sustainable rice cultivation in the face of changing climatic conditions.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Theoretical implications:\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eAdvancement in climate modeling techniques\u003c/strong\u003e \u003cp\u003eThe study contributes theoretically by utilizing advanced climate modeling techniques, specifically the CMIP6 framework. This enhances the theoretical understanding of applying climate models to project future scenarios, providing more precise predictions of climate change impacts on rice cultivation in specific areas.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eValidation and refinement of climate change hypotheses\u003c/strong\u003e \u003cp\u003eTheoretical implications include the validation and refinement of hypotheses related to the impact of global warming on thermal stress indices. Our findings strengthen existing theories about the consequences of climate change, offering empirical evidence for the projected increase in extreme heat stress.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eIntegration of multiple climate scenarios\u003c/strong\u003e \u003cp\u003eThe study considers that both medium (SSP2-4.5) and high (SSP5-8.5) emission scenarios contribute theoretically by acknowledging the uncertainty in future climate conditions. This aligns with the theoretical understanding that climate projections should encompass different potential trajectories based on socioeconomic factors, providing a comprehensive basis for policy formulation.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eInsights into reproductive stage vulnerability\u003c/strong\u003e \u003cp\u003eTheoretical implications extend to a deeper understanding of the vulnerability of rice plants during the reproductive stage. The study contributes theoretical insights into the intricate relationship between temperature dynamics and crop yield by highlighting specific panicle initiation, flowering, and pollination stages as highly susceptible to heat stress.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eIn summary, the practical implications offer actionable steps for immediate implementation, while the theoretical implications contribute to the broader scientific understanding of climate change dynamics and modeling techniques. Both aspects are crucial for developing effective strategies to address the challenges posed by changing climate conditions in the northeastern region of Bangladesh.\u003c/p\u003e \u003c/div\u003e"},{"header":"6. Limitations and Future Research Direction","content":"\u003cp\u003eThis study only considered historical phenological knowledge to compute future extreme climatic indices for the reproductive stage of rice in the northeastern region of Bangladesh. It did not address the potential alterations in rice phenology due to a warmer environment and agronomic practices in the future. Furthermore, our study did not predict rice yield in future situations under changing climate scenarios. The projection of future phenology, timing, and results relies on implementing appropriate management policies according to how climate change emerges. Additional research is needed to utilize a phenological model to accurately predict the impact of a warmer climate and agronomic techniques on rice phenology. Moreover, it is necessary to explore the alteration of climatic extremes considering the various growth stages in the northeastern \u003cem\u003ehaor\u003c/em\u003e basin of Bangladesh. Hence, our findings may exaggerate the adverse consequences of heat extremes while underestimating the negative impacts of cold extremes.\u003c/p\u003e \u003cp\u003eFlash floods are a major concern for rice production during the northeastern region's premonsoon (MAM) season (Nowreen et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In this study, we used an extreme rainfall index called HR days (\u0026gt;\u0026thinsp;20 mm/day) but found no significant change. The northeastern part of Bangladesh is encompassed by the mountain regions Meghalaya, Tripura, and Assam of India, with many of the most precipitation-rich locations on the planet within a few hundred kilometers of its boundary. Studies have explored whether flash floods in the northeastern \u003cem\u003eHaor\u003c/em\u003e basin resulted from a rapid influx of water into the rivers due to the heavy precipitation over a shorter period in the basin's upper part (Dey et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Future research endeavours should prioritize evaluating extreme rainfall indices within both the upper and lower Meghna River catchments to address the issue of flash floods more effectively.\u003c/p\u003e"},{"header":"7. Conclusion","content":"\u003cp\u003eThe northeastern part of Bangladesh, known for its unique hydroecological attributes, faces significant challenges in changing climate patterns. This study's analysis of extreme climatic indices during the rice anthesis and grain filling stage reveals a trend of increasing heat stress, which could adversely affect \u003cem\u003eboro\u003c/em\u003e rice production in Bangladesh, a vital agricultural activity in the region. The reproductive phase of rice is susceptible to extreme temperatures, and the projected rise in heat stress indices during this critical phase could lead to reduced yields.\u003c/p\u003e \u003cp\u003eTo address these challenges, it is imperative to prioritize adaptive measures. Heat-tolerant rice cultivars and adjustment of planting time through agronomic management practices could help mitigate the adverse impacts of rising temperatures on rice production in the northeastern region of Bangladesh. Additionally, further research is needed to develop comprehensive phenological models that consider both climatic extremes and agronomic practices for a more accurate prediction of future rice yields.\u003c/p\u003e \u003cp\u003eFurthermore, the study did not find significant changes in heavy rainfall days despite the region's susceptibility to flash floods during the premonsoon season. Future research should explore extreme rainfall indices in both the upper and lower Meghna River catchments to better understand and address the issue of flash floods in the northeastern part of Bangladesh.\u003c/p\u003e \u003cp\u003eHowever, this research underscores the importance of proactive adaptation strategies in the northeastern region of Bangladesh to secure the livelihoods of its inhabitants, who heavily rely on rice production, and to ensure food security in the face of changing climate patterns.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eWe acknowledge the Cereal Systems Initiative for South Asia project (https://csisa.org) funded by the United States Agency for International Development (USAID) for assisting in preparing this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMohammad Kamruzzaman:\u0026nbsp;Conceptualization, Methodology, Software. HM Touhidul Islam: Data curation, Visualization, Investigation Writing- Original draft preparation.\u0026nbsp;Md. Sazzadur Rahman\u003cem\u003e:\u003c/em\u003e Supervision Sharif Ahmed: Software, Review and Editing and Validation. Liala Ferdousi Lipi, Md. Arifur Rahman Khan, Lam-Son Phan Tran, and AMK Zakir Hossain: Writing- Reviewing and Editing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from funding agencies in public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003eEthics approval\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNot applicable.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn response to a formal request, we will provide the requested data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of interest statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbbas F, Rehman I, Adrees M, et al (2018) Prevailing trends of climatic extremes across Indus-Delta of Sindh-Pakistan. 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J Exp Bot 62\u003c/li\u003e\n\u003cli\u003eZhao W, Chou J, Li J, et al (2022) Impacts of Extreme Climate Events on Future Rice Yields in Global Major Rice-Producing Regions. Int J Environ Res Public Health 19:. https://doi.org/10.3390/ijerph19084437\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-climatology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taac","sideBox":"Learn more about [Theoretical and Applied Climatology](https://www.springer.com/journal/704)","snPcode":"704","submissionUrl":"https://submission.nature.com/new-submission/704/3","title":"Theoretical and Applied Climatology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Extreme climate events, climate change, Flash floods, future projections, heat-tolerant varieties, growth duration, yield losses","lastPublishedDoi":"10.21203/rs.3.rs-4007462/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4007462/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eNortheastern Bangladesh is highly vulnerable to the intensifying impacts of climate change, with extreme climatic events posing a significant threat to rice production. This study examines the projected changes in five key thermal stress indices and heavy rainfall during the rice reproductive phase using 15 global climate models (GCMs) under moderate (SSP2-4.5) and high (SSP5-8.5) emission scenarios. Statistical downscaling and bias correction techniques were employed to generate daily climate data for rainfall, maximum temperature (Tmax), and minimum temperature (Tmin). The Mann\u0026ndash;Kendall (MMK) test was applied to identify future trends in these extreme events. The results reveal a substantial decrease in cold stress indices, with three consecutive cold days (CCD3) and six consecutive cold days (CCD6) projected to decline by approximately 9 days. Notably, heat stress indices are anticipated to increase, with hot days (HD) and consecutive hot days (CHD) rising by 18 and 11, respectively. Heavy rainfall days (HR) did not exhibit significant changes. The projected rise in temperatures above 35\u0026deg;C during the rice reproductive phase, encompassing critical stages such as flowering, gametophyte development, anthesis, and pollination and fertilization, suggests adverse consequences for rice yields. These findings underscore the urgency of implementing specific adaptation and mitigation measures to minimize potential yield losses in a future characterized by elevated temperatures. Such measures may include cultivating heat-tolerant rice varieties, adjusting planting windows, and diversifying rice varieties with varying growth durations.\u003c/p\u003e","manuscriptTitle":"Assessing the Impacts of Future Climate Extremes on Boro Rice Cultivation in the Northeastern Haor Region of Bangladesh: Insights from CMIP6 Multi-Model Ensemble Projections","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-06 19:04:37","doi":"10.21203/rs.3.rs-4007462/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2024-07-08T09:20:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-03T14:41:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"23577870373938957857479733177816759383","date":"2024-07-02T17:42:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"302601976558318498820457912490800593328","date":"2024-06-30T19:30:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"3435363972974360129516673307316369678","date":"2024-06-29T14:16:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"240268454821649471791057627028094166678","date":"2024-06-29T05:35:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"95690868223187539656822252322302230238","date":"2024-06-28T08:19:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"238049417598155418807631393628179372748","date":"2024-06-27T18:21:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"85855635946889901919752582577487789805","date":"2024-06-27T17:25:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"38a84966-8b15-40e7-95a6-7fb2fd4f6f5b","date":"2024-04-26T02:19:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"98ddc8c0-702b-43c2-938e-e3d215d930e0","date":"2024-03-09T12:34:04+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-03-09T10:49:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-03T21:52:38+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-03T21:52:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"Theoretical and Applied Climatology","date":"2024-03-03T03:46:21+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-climatology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taac","sideBox":"Learn more about [Theoretical and Applied Climatology](https://www.springer.com/journal/704)","snPcode":"704","submissionUrl":"https://submission.nature.com/new-submission/704/3","title":"Theoretical and Applied Climatology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"750c8ffc-af28-4373-bf13-0a2db3690564","owner":[],"postedDate":"March 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-09T16:10:39+00:00","versionOfRecord":{"articleIdentity":"rs-4007462","link":"https://doi.org/10.1007/s00704-024-05270-5","journal":{"identity":"theoretical-and-applied-climatology","isVorOnly":false,"title":"Theoretical and Applied Climatology"},"publishedOn":"2024-12-06 15:57:46","publishedOnDateReadable":"December 6th, 2024"},"versionCreatedAt":"2024-03-06 19:04:37","video":"","vorDoi":"10.1007/s00704-024-05270-5","vorDoiUrl":"https://doi.org/10.1007/s00704-024-05270-5","workflowStages":[]},"version":"v1","identity":"rs-4007462","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4007462","identity":"rs-4007462","version":["v1"]},"buildId":"rHA-KDH7Qsr4HCuvH75dn","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}