Reveals of a Shifting Climate: A Regional Analysis of Rainfall and Temperature Trends at Mymensingh Division in Bangladesh (1950-2020)

Reveals of a Shifting Climate: A Regional Analysis of Rainfall and Temperature Trends at Mymensingh Division in Bangladesh (1950-2020) | 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 Reveals of a Shifting Climate: A Regional Analysis of Rainfall and Temperature Trends at Mymensingh Division in Bangladesh (1950-2020) Md Khairul Haque, Md Rakibul Hassan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5098521/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 21 You are reading this latest preprint version Abstract Professional resource allocation and planning in response to climate change in developing regions such as the Mymensingh Division of Bangladesh requires comprehension of trends in temperature and precipitation over long periods of forecast. This is the reason why this study examines the temperature and precipitation from the years 1950 to 2020 in order to provide a reasonable view of local climatic conditions and facilitate the policymaking process. By using climactic research unit (CRU) TS data sets in creation of raster layers using ArcGIS tools we undertook data processing research which involved statistical analysis methods. Mann-Kendall test has generated a very encouraging result as it has found relative increase in annual precipitation, averaging about 2760.52 mm and oscillating between 1752 mm and 4338 mm. Kendall’s tau correlation τ = 0.156, p-value = 0.024, shows a possible change over a period of time. Slope of sen demonstrated that precipitation regime has increase by 1.9 mm annually. The analyses of autocorrelation and partial autocorrelation confirmed that the precipitation data upside and trends are clearly delineated. Progressive warming trend as regards the average annual temperature was observed, as the years went by, the average annual temperature increased from 24.77 0 c to 25.170c, more so in recent years where there have been high degree of warming. This study highlights the need for ongoing climate and the enhancement of global warming policies to prevent worsening situations. Climate change IDW Non-Parametric Test Annual temp. and rainfall trends Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Climate change represents a significant global challenge, particularly affecting nations that are still developing. The parameters of temperature and precipitation are fundamental elements that shape ecological systems, with their fluctuations exerting a profound influence on climatic patterns. It is imperative to comprehend the long-term trajectories of these elements for the effective management of hydrological resources, strategic agricultural planning, and adaptation to the ramifications of climate change (Mohd Wani et al. 2017 ; Alemayehu et al. 2020 ). The formulation of effective adaptation strategies necessitates a thorough examination of long-term trends and extremes in regional climatic variables (Atube et al. 2022). In the Mymensingh Division of Bangladesh, the analysis of temperature trends over prolonged durations is essential, given the region's economic importance.Projections indicate that global surface temperatures may increase by 1.4 to 5.8°C between 1990 and 2100, with possible escalations of 1.5°C to over 2.0°C by the century's conclusion (Alam et al. 2023 ).A comprehensive understanding of these patterns is vital for promoting sustainable agricultural practices, effective disaster management, and climate adaptation strategies in Bangladesh. The impacts of climate change are heterogeneous, influenced by geographic and socioeconomic variables. Anticipated future climatic effects may heighten the frequency and intensity of floods and droughts, with temperature increases projected to range from 3 to 4°C (Esayas et al. 2018 ). The variability in precipitation, influenced by both natural and anthropogenic factors, has significant implications for water resources, agricultural productivity, and ecosystems.Such variability complicates efforts in water management and mitigation strategies (Ahmad et al. 2022).Historical analyses of precipitation trends reveal increases in areas such as eastern North America and northern Europe, whereas declines have been recorded in the Sahel, Mediterranean region, and southern Asia (Odedra and Jadeja 2023 ). Rainfall patterns exhibit substantial temporal and spatial variability, driven by seasonal atmospheric alterations and geographic characteristics (Rani 2014 ). Detailed assessments of temperature and precipitation trends are vital for grasping and addressing the repercussions of climate change, in light of the interconnected characteristics of these phenomena and their global impacts.South Asia, including Bangladesh, experiences significant temperature variability, presenting challenges in identifying climate change and discerning its anthropogenic effects (Priyakumara and Ranasinghe 2023 ). Rising temperatures are projected to exacerbate challenges related to floods, droughts, and cyclones, with internal climatic fluctuations such as ENSO also contributing to flooding occurrences (Uddin et al. 2023). Comprehensive investigations of rainfall are crucial for effective water management and disaster preparedness. However, there exists a deficiency of localized studies addressing spatiotemporal rainfall variability within Bangladesh.Urban ecosystems deliver essential services, and their functionality is profoundly affected by climate change (Pandey and Ghosh 2023 ). Fluctuations in precipitation have a significant impact on food security, agricultural productivity, and the associated risks of droughts and floods.A report by the Asian Development Bank and the International Food Policy Research Institute (2009) identified Bangladesh as extremely susceptible to climate change, based on indicators related to alterations in temperature and precipitation, agricultural labor dynamics, and poverty levels (Billah et al. 2015). The challenge of addressing climate change necessitates localized analyses to facilitate informed decision-making and the development of effective adaptation strategies (Dadi et al. 2024 ).Alleviating the effects of climate change requires a cutback on local emissions, the implementation of greenhouse gas reduction strategies, and the enhancement of infrastructure resilience. Comprehension of regional climate variability and transitions is imperative for enhancing climate projections and formulating effective adaptation strategies. Employing statistical methodologies for the analysis of trends and variability in temperature and precipitation data is fundamental to this endeavor (Manglem 2021 ). Investigative research into temperature and precipitation fluctuations is crucial for urban areas that are experiencing the impacts of climate change (Balogun et al. 2023 ). This study is centered on the Mymensingh Division, scrutinizing long-term trends in temperature and precipitation from the year 1950 to 2020. It examines temperature patterns, analyzes the correlation between temperature and precipitation variability, and offers insights to inform policy and adaptation strategies in response to a dynamically changing climate. Materials and Methods Study Area Mymensingh division is located in northern Bangladesh and is bordered by Meghalaya (India) to the north, the district Gazipur to the south, Netrokona and Kishoreganj districts to the east, and Sherpur, Jamalpur, and Tangail districts to the west. Mymensingh division is a metropolitan city of Bangladesh located on the bank of the old Brahmaputra River about 120 km north of the national capital city Dhaka. It lies on 24°45′14″N 90°24′11″E. Mymensingh Division covers an area of about 10,485 (kilometers) 2 and it has a population of about 12,225,498. The division Mymensingh’s climate is closer to the Himalayan Mountain that’s why their temperature is a little colder than the capital city Dhaka's temperature. Mymensingh occupies a Tropical wet and dry or savanna climate. The maximum amount of rainfall occurs during the monsoon season (May/June to August). The area falls under monsoon climatic repossess and annual temperature differs from 13ºC to 37ºC average monthly rainfall: January 12mm, June 469mm, December 2mm, and most the time total annual rainfall varies from 2000mm to 2500mm. Data sources CRU TS (Climatic Research Unit gridded Time Series) is a broadly used climate dataset on a 0.5° latitude by 0.5° longitude grid over all land domains over the world except Antarctica. It is extracted by the interpolation of monthly climate irregularities from extensive networks of weather station observations (Harris et al. 2020 ). Data processing The principle of the methodology lies in the application of certain ArcGIS tools. The "Make Net CDF Raster Layer" tool is used to create a raster layer from Net CDF files, while "Raster Processing" tools make possible the manipulation, editing, and management of raster data. To analyze cell values across multiple raster, the "Cell Statistics" tool is employed to derive statistical information. Besides, the "Inverse Distance Weighted (IDW)" tool comes into play for interpolating raster surfaces from point data using a reverse distance weighted technique. Following the application of these tools, the processed data is exposed to analysis and visualization, leveraging ArcGIS mapping and visualization capabilities to present the results in a relevant manner. Rainfall Pattern Analysis with Non-parametric Statistical Techniques Menn- Kendal Analysis: Rainfall (mm) The Mann-Kendall test, also referred to as Kendall's statistic, is a non-parametric experiment that is mainly used to repress randomness in trends in climatology and hydrology (Odedra and Jadeja 2023 ). The Mann-Kendall statistics ‘S’ is given as: S = \(\:\sum\:_{i=1}^{n-1}{\sum\:}_{j=i+1}^{n}Sgn\:(xj-xi)\) When statistic ‘S’ is positive, the data is drifting upward, and when it is negative, the data is trending lower. The variance of the Mann-Kendall statistics for sample sizes n ≥ 8 is given by Var (S) = \(\:\frac{[n\left(n-1\right)\left(2n+5\right)-{\varSigma\:}_{t}\left(t-1\right)\left(2t+5\right)]\:}{18}\) Where ‘t’ is referred to as the extent of any tie. Sen’s Slope A straightforward non-parametric method created by Sen in 1968 can be used to examine the true slope of a time series if it has a linear trend. In the sample of N data pairs, the trend line's slope is to be calculated by Q = \(\:\frac{xj-xi}{j-i}\) Where x j and x i are the data values at times j and i (j > i) respectively. The median of these ‘N’ values of Q is Sen’s estimator of slope which is calculated as 𝛃 = Q \(\:\frac{N+1}{2}\) ; if N is odd. 𝛃 = \(\:\frac{Q\left(\frac{N}{2}\right)+Q\left(\frac{N+2}{2}\right)}{2}\) ; if N is Even. Ultimately, β is calculated using a two-sided experiment with a 100 (1-α) % confidence interval, and the non-parametric test can then be used to find out the true slope. A rising or rising trend in a specific time series is shown by a positive value of β, and a falling or decreasing trend is shown by a negative value of β. Auto-correlation Tests It is mandatory to find out autocorrelation in the time series of the data before performing a trend test on rainfall data. A rough calculation of the population autocorrelation coefficient is the sample autocorrelation coefficient (Saikh et al. 2023 ). Both the successive coefficient and the lag-1 autocorrelation coefficient can be calculated (Piyoosh and Ghosh 2016). However, a worldly most used indicator of time series dependency is the first serial correlation coefficient to ascertain the existence of serial independence (Basistha et al. 2009 ; Mondal et al. 2015 ). An analysis is supervised by contrasting the possible hypothesis with the null hypothesis. The alternative hypothesis is accepted if there is a notable rejection of the null hypothesis. To investigate this, a significance threshold of 5% is considered suitable. Result and Discussion Rainfall Analysis Descriptive Analysis of Rainfall Data The rainfall data under analysis comprises 96 observations, all of which are complete with no missing values. The descriptive statistics, as shown in Table 1 , indicate that the minimum recorded annual rainfall was 1752 mm, while the maximum reached 4338 mm. The mean annual rainfall was calculated at 2760.52 mm, with a standard deviation of 551.27 mm, indicating variability in rainfall patterns across the years. The relatively high standard deviation suggests that there is considerable fluctuation in annual rainfall, which is critical for understanding climate dynamics and water resource management in the region. Table 1 Descriptive Statistics of Rainfall Data Variable Observations Obs. with missing data Obs. without missing data Minimum Maximum Mean Standard Deviation Rainfall (mm) 96 0 96 1752.000 4338.000 2760.521 551.269 Decadal Variation in Rainfall In Fig. 3 (a), which shows data from 1951 to 1960, the greatest recorded rainfall was between 2025 mm and 2285 mm, while the lowest amounts were between 3060 mm and 3317 mm. During this time, Bangladesh's average annual rainfall was roughly 2200 mm. Rainfall patterns from 1951 to 1960 are depicted in Fig. 3 (b), with most values similar to the national average, though certain areas experienced above-normal rainfall. Figure 3 (c) illustrates rainfall data from 1961 to 1970, revealing lower-than-average rainfall with values ranging from 1988 mm to 2380 mm. In contrast, the 1980s, as seen in Fig. 3 (d), had near-average rainfall, with peak values ranging from 1952 mm to 2208 mm. Figure 3 (e) displays the decade of 1981–1990, when maximum rainfall ranged from 2135 mm to 2418 mm, closely reflecting regional temperature fluctuations. The rainfall from 1991 to 2000, as shown in Fig. 3 (f), was comparable with the national average. The highest recorded rainfall from 2001 to 2010, as indicated in Fig. 3 (g), was between 1829 and 2103 mm. Finally, Fig. 3 (h) shows the period from 2011 to 2020 when the minimum recorded rainfall was between 3360 mm and 3630 mm, which was higher than the national average. Rainfall trends from 1951 to 2010 show oscillations, with values near the national average from 1951 to 1960, below-average rainfall between 1988 mm and 2380 mm in 1961–1970, near-average levels peaking at 2208 mm in the 1980s, and rainfall ranging from 1829 mm to 2418 mm between 1981 and 2010 (Tanvir 2019). Mann-Kendall Trend Test Mann-Kendall trend test / 2-tailed test (mm) ; Where, Kendall’s tau = 0.156 S = 713 Var(S) = 99812.333 P -value (two-tailed) = 0.024 Alpha (α) = 0.05 An approximation has been used to compute the p-value. Test Interpretation Hypothesis H0: No trend exists in the series. Ha: A trend exists in the series. The p-value (0.024) is below the significance level (α = 0.05), so we reject H0 and accept Ha. The study involved continuity correction and tie corrections. The Mann-Kendall trend test found a substantial rising trend in yearly rainfall (Kendall's tau = 0.156). This finding suggests a good trend in rainfall during the research period(Mondal et al. 2012) found a similar positive trend in northeastern Bangladesh with a Kendall's tau of 0.18, while Rahman et al. (2017) found a somewhat weaker trend (Kendall's tau = 0.12) in their study. Our findings suggest a slightly stronger trend, which may represent changing climatic circumstances caused by global climate change. This supports earlier research and has importantimplications for water resource management, agriculture, and flood risk. The coherence with current research emphasizes the importance of continuous monitoring to understand and address the effects of climate variability. More research is needed to understand the causes of these changes and their long-term repercussions. Sen’s Slope Estimation The Sen's Slope analysis as presented in Table 2 , indicates a positive trend in annual rainfall with an average increase of 1.9 mm per year over the study period from 1950 to 2020. This analysis which considers variability across different decades, suggests a significant shift in rainfall patterns in the region. Table 2 Confidence interval for Sen’s Slope estimation of Rainfall Trends Value Lower Bound (95%) Upper Bound (95%) Slope 1.900 0.920 11.433 Intercept -1021.000 -10512.167 -60.600 Sen's slope analysis of annual rainfall from 1950 to 2020 indicates an average increase of 1.9 mm/year. Sensitivity analysis, depicted in Fig. 4 , involves creating a rainfall model and systematically varying input parameters, such as meteorological conditions and land use, to assess their impact on rainfall outputs. This approach, using methods like factorial design or variance-based techniques, helps evaluate model robustness, precision in forecasting, and the uncertainty of rainfall projections. The observed Sen's slope of 1.9 mm/year in this study contrasts with a lower 1.2 mm/year found in other studies across Bangladesh (Ahmed et al. 2019) and is closer to a 2.1 mm/year increase reported in northern Bangladesh (Hossain et al. 2020), highlighting regional variations in rainfall trends. Table 3 Data Table for Autocorrelation and partial correlation for rainfall trends from 1950 to 2020. Figure 5 (a): The autocorrelogram shows the temporal correlation of rainfall data for Mymensingh Division from 1950 to 2020. The x-axis represents time lags in years, and the y-axis displays the correlation coefficient, indicating the strength and direction of relationships between observations at different lags. Strong positive correlations at certain lags suggest persistent patterns, hinting at seasonal cycles or long-term trends, while weak correlations indicate randomness (Ahmed et al. 2019). Figure 5 (b): The partial autocorrelogram controls for intermediate observations to highlight the direct effects of historical rainfall measurements on current values. This method isolates significant temporal dependencies, offering clearer insights into the key lags influencing rainfall patterns in the region. Temperature Analysis Decadal Analysis of Temperature Data The analysis of annual temperature data for Mymensingh Division from 1950 to 2020, given in Fig. 6 , demonstrates significant decade-wise variability. From 1950 to 1960, maximum temperatures were between 24.77°C and 25.06°C, while minimum temperatures ranged between 23.55°C and 23.85°C, which was lower than the area average of 27.05°C. In Fig. 6 (b), greater temperatures were reported from 1961 to 1970, with maximum values ranging from 25.77°C to 26.08°C. The 1970s, represented in Fig. 6 (c), exhibited a consistent range of 25.52°C to 25.62°C. Figure 6 (d) depicts a little cooling in the 1980s, with maximum temperatures ranging from 24.99°C to 25.26°C and minimums ranging from 23.85°C to 24.13°C. Temperatures ranged between 24.81°C and 25.11°C from 1991 to 2000, according to Fig. 6 (e). Temperatures throughout the 2000s, as indicated in Fig. 6 (f), ranged between 24.77°C and 25.08°C. Figure 6 (g) shows that temperatures increased between 2011 and 2020, ranging from 24.89°C to 25.17°C, mirroring recent warming trends. Table 4 Summary of Rainfall Trends in Mymensingh Division (1950–2020) Year Min Rainfall (mm) Max Rainfall (mm) Mean Rainfall (mm) Std Deviation 1950 1766 3317 2542.00 441.79 1960 2018 3382 2700.25 388.24 1970 1988 4338 2772.67 484.71 1980 1752 3121 2216.83 497.42 1990 2135 3837 2703.67 468.60 2000 1908 3655 2491.67 461.36 2010 1829 3475 2377.17 497.42 2020 2009 3630 2279.67 468.60 Table 4 depicts annual rainfall variations in Mymensingh Division from 1950 to 2020, with significant oscillations in minimum (1752 mm in the 1980s) and maximum (4338 mm in the 1970s) rainfall, as well as changes in standard deviation. These changes reflect times of extreme wetness and dryness, which is consistent with previous research on Bangladesh's rainfall trends. The standard deviation demonstrates variability, with some decades having more constant rainfall than others. Figure 7 shows the mean temperatures for the same time period, which ranged from 23.55°C to 26.65°C. Temperatures remained steady throughout the 1950s and 1960s, but a gradual warming began in the late 1960s. After a minor decline in the 1980s, temperatures surged again, peaking between 2010 and 2020. Table 5 Temperature Patterns and Statistical Summary for Mymensingh Year Minimum Temperature Maximum Temperature Mean Temperature Standard Deviation 1950 23.5519 25.3581 24.7017 0.3806 1960 24.1529 26.0833 25.6494 0.2971 1970 25.0523 25.7333 25.49 0.1301 1980 22.42 25.22 25.93 0.6092 1990 23.85 25.54 24.94 0.3623 2000 23.5689 25.4161 24.7404 0.3852 2010 24.7679 26.6502 25.9947 0.3964 2020 23.7174 25.4591 24.8371 0.3727 The regional temperature statistics for Mymensingh Division from 1950 to 2020, displayed in Table 5 , reveal significant changes in mean, minimum, and maximum temperatures, with varied standard deviations. These changes represent the region's changing climate during the last seventy years. The data show significant fluctuations in temperature extremes, which are likely impacted by broader climatic variables. From 1950 to 1960, the mean temperature rose while both minimum and maximum temperatures increased. This rising trend continued throughout the 1970s, but the standard deviation fell, indicating less fluctuation in temperature extremes. The 1980s experienced a decrease in both minimum and maximum temperatures, while the mean temperature remained consistent, albeit with more variability as indicated by a larger standard deviation. Temperatures fluctuated during the 1990s, with a minor rise in mean temperature and considerable fluctuations in minimum and maximum temperatures. Temperatures fell slightly throughout the 2000s, but the latter half of the decade and the 2010s saw a dramatic increase in both minimum and maximum temperatures, in accordance with worldwide warming trends. The observed increase in mean temperature, combined with rising yearly rainfall, supports a more variable climate, echoing previous studies linking temperature increases to rainfall variability. This study's temperature rise of 0.02°C/year exceeds Bangladesh's recorded increase of 0.015°C/year. These developments are consistent with broader patterns in South Asia, where global warming is causing increased temperature and rainfall variability. Temperature Trends and Rainfall Variability Effects The climatic conditions observed in the Mymensingh Division from the year 1950 to 2022 exhibited considerable fluctuations in both precipitation and temperature. In the decade of the 1950s, the annual rainfall experienced a substantial increase from 1766 mm to 3317 mm by the 1960s, ultimately reaching a peak of 3655 mm in the 2000s before stabilizing at 3630 mm in the 2020s. Rainfall levels remained relatively stable throughout the decades of the 1970s and 1980s, characterized by only minor fluctuations. The temperature, which experienced a slight increase during the 1950s and 1960s, witnessed a significant escalation in the 1980s, attaining its zenith in the late 1980s. This upward trajectory in temperature persisted throughout the 1990s and 2000s, coinciding with an increase in precipitation levels. In the most recent decades, the trends pertaining to both temperature and rainfall have continued to ascend, indicating a potential correlation. Decadal and autocorrelation analyses have unveiled noteworthy alterations in climatic patterns, underscoring the variability of precipitation (Shahid 2010 ) and the periodic fluctuations in temperature (Hossain et al. 2018 ). These findings are consistent with prior research on climate variability. Extreme Climatic Events Occurred in Mymensingh Division from 1950 to 2020 The exploration of severe weather phenomena in Mymensingh Division (1950–2020) uncovers intriguing links between escalating temperatures and the surge in rainfall. Particularly, the late 1980s marked a period where temperature and precipitation soared in tandem, suggesting intricate climate dynamics (Karmakar and Shrestha 2000 ). During the decade spanning the 1950s to the 1960s, annual rainfall dramatically escalated from 1766 mm to an impressive 3317 mm, with temperatures also climbing steadily. By the twilight of the 2000s, annual rainfall soared to 3655 mm, a trend that persisted into the 2010s and 2020s (Shahid 2010 ). The 1970s and 1980s exhibited consistent rainfall, yet temperatures reached their zenith in the late 1980s.These observed trends resonate with the broader narrative of global climate change, underscoring the heightened occurrence of extreme weather phenomena in Bangladesh. Mymensingh's susceptibility to these climatic alterations accentuates the urgent requirement for effective water management and resilient infrastructure. Conclusions Rainfall and temperature statistics for Mymensingh Division from 1950 to 2020 show major climatic shifts with far-reaching ramifications for water supplies, agriculture, and ecosystems. The results show a significant increase in both temperatures and annual rainfall. The Mann-Kendall trend test finds a Kendall's tau of 0.156 with a p-value of 0.024, while Sen's Slope analysis shows a 1.9 mm annual rise in rainfall (Ahmad et al., 2023 ; Akter et al., 2023 ). This rainfall variability, which encompasses both extreme wet and dry spells, highlights the region's increased sensitivity to changing climate patterns. This observation is consistent with global trends (Harris et al., 2020 ; Alam et al., 2023 ). The higher trend in temperatures found in this study is consistent with global warming predictions, highlighting the crucial need for effective ways to reduce and adapt to these climatic changes (Rahman & Lateh, 2017 ; Karmakar & Shrestha, 2000 ; Shahid, 2010 ). The increase in temperature and rainfall creates a complicated combination of difficulties for water supply reliability, agricultural production, and ecosystem health. Addressing these difficulties necessitates a comprehensive strategy that includes both mitigation and adaptation measures. A thorough understanding of regional climate dynamics is critical for formulating evidence-based policy and effective response strategies. Continued study and observation are required to improve our understanding of how climate change affects local ecosystems and human systems. This study gives unique insights into the climatic shifts affecting Mymensingh Division, emphasizing the need of incorporating these findings into policy and planning. Using this knowledge, stakeholders can develop tailored interventions to address specific risks, encourage sustainable behaviors, and boost resilience across many sectors. Furthermore, preventive interventions based on reliable data might help predict future issues and mitigate potential dangers linked with ongoing climate variability. The integration of scientific discoveries into practical solutions will be critical for navigating the changing climate scenario and ensuring the region's long-term sustainability. Declarations Ethics Approval and Consent to Participate This is not relevant to the current study Consent for Publication This is not relevant to the current study. Availability of Data and Material The datasets employed in this investigation are sourced from publicly accessible repositories, namely the CRU TS (Climatic Research Unit gridded Time Series) dataset. The CRU TS dataset is readily available through the Climatic Research Unit's archive at the University of East Anglia and can be retrieved at https://crudata.uea.ac.uk/cru/data/hrg/. Supplementary processed data and materials utilized throughout the present study can be obtained from the corresponding author upon reasonable inquiry. Competing Interests The authors have disclosed that there are no potential conflicts of interest. Funding This study received no outside funding. Authors' Contributions The manuscript was carefully crafted through the collaborative efforts of all listed authors. KH Haque took on the main responsibility for writing the initial draft and conducting the data analysis, ensuring that the research findings were presented with both rigor and accuracy. RK Hassan, who serves as an Assistant Professor in the department, made significant contributions to the review and editorial processes, thus ensuring that the manuscript met high academic standards. All authors worked closely together throughout the research project and have approved the final version of the manuscript. Acknowledgements We are deeply grateful to Md. Aminul Islam for his meticulous editing and evaluation, both of which significantly raised the manuscript's caliber. We also thank Md. Fazle Rabbi for his assistance with data reorganization and insightful feedback during the development of this study. We are grateful to the University of East Anglia for supplying high-resolution gridded datasets of historical temperature and rainfall data, which were critical to the study's success. Contributions of Authors The manuscript was developed through the collaborative efforts of all listed authors. 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Bull Environ Pharmacol Life Sci 6 Kumar S (2023) Climate Change in Punjab: Long-term Trends of Temperature & Rainfall. https://www.researchgate.net/publication/375163416 Karmakar S, Shrestha M L (2000) Recent climatic changes in Bangladesh. SMRC 4: 1-43. Kwarteng A Y, Dorvlo A S, Kumar G T V (2009) Analysis of a 27-year rainfall data (1977-2003) in the Sultanate of Oman. International Journal of Climatology 29(4):605–617.doi: 10.1002/joc.1727 Lázaro R, Rodrigo F S, Gutiérrez L, Domingo F, Puigdefábregas J (2001) Analysis of a 30-year rainfall record (1967-1997) in semi-arid SE spain for implications on vegetation. Journal of Arid Environments, 48(3): 373–395. doi: 10.1006/jare.2000.0755 Maina J, Wandiga S, Gyampoh B, Charles G (2020) Analysis of Average Annual Rainfall and Average Maximum Annual Temperature for a Period of 30 years to Establish Trends in Kieni, Central Kenya Manglem S A (2021) Climate Variability and Trend Analyses of Rainfall and Temperature Data of Imphal, Manipur. Disaster Advances 14(11): 57–63. doi: 10.25303/1411da5763 Meisner C A, Ali M Y (2023) Bangladesh Under Possible Climate Change Threat. In World Regional Geography Book Series F3079:119–127). doi: 10.1007/978-3-031-45093-8_13 Mohd Wani J, Sarda V K, Jain Sanjay K (2017) Assessment of Trends and Variability of Rainfall and Temperature for the District of Mandi in Himachal Pradesh, India. Slovak Journal of Civil Engineering 25(3):15–22. doi: 10.1515/sjce-2017-0014 Mondal A, Khare D, Kundu S (2015) Spatial and temporal analysis of rainfall and temperature trend of India. Theoretical and Applied Climatology 122(1–2):143–158. doi:10.1007/s00704-014-1283-z Muhammad N S, Abdullah J, Julien P Y (2020) Characteristics of Rainfall in Peninsular Malaysia. Journal of Physics: Conference Series 1529(5). doi: 10.1088/1742-6596/1529/5/052014 Obot N I, Chendo M A C, Udo S O, Ewona I O (2010) Evaluation of rainfall trends in Nigeria for 30 years (1978-2007). In International Journal of the Physical Sciences (Vol. 5, Issue 14). http://www.academicjournals.org/IJPS Odedra K N, Jadeja B A (2023) Rainfall Variability Trend in Porbandar, Gujarat (Vol. 1, Issue 2). Pandey B, Ghosh A (2023) Urban ecosystem services and climate change: a dynamic interplay. Front Sustain Cities 5. doi: 10.3389/frsc.2023.1281430. Povak N A, Manley P N,Wilson K N (2023) Integrating climate adaptation strategies in spatial decision support systems. USDA-FS, Pacific Southwest Research Station. doi: 10.21203/rs.3.rs-3030269/v1 Povak N A, Manley P N (2023) Evaluating climate change impacts on ecosystem resources through the lens of climate analogs. Frontiers in Forests and Global Change, 6. doi: 10.3389/ffgc.2023.1286980 Priyakumara U L A S, Ranasinghe E M S (2023) Rainfall Variability in Ratnapura District: A Comparative Study of the Two 30-year Standard Periods of 1961-1990 and 1991-2020. Sri Lanka Journal of Social Sciences and Humanities 3(2):165–177. doi:10.4038/sljssh.v3i2.109 Pujiastuti I,Nurjani E (2018) Rainfall pattern variability as climate change impact in the Wallacea Region. IOP Conference Series: Earth and Environmental Science 148(1). doi: 10.1088/1755-1315/148/1/012023 Rahman M R, Lateh H (2017) Climate change in Bangladesh: a spatio-temporal analysis and simulation of recent temperature and rainfall data using GIS and time series analysis model. Theoretical and Applied Climatology 128(1–2): 27–41. doi:10.1007/s00704-015-1688-3 Mutunga E J, Ndungu C, Mwangi M, Kerandi N M (2022) Rainfall and temperature trends and variability in arid and semi-arid lands of Kitui County, Kenya. Journal of Environment and Earth Science 12(12). doi: 10.7176/JEES/12-12-05 Rani A H, Shreedhar R (2014) Study of rainfall trends and variability for Belgaum district. International Journal of Research in Engineering and Technology, (Special Issue 06) 148 Saikh N I, Saha S, Sarkar D, Mondal P (2023) Rainfall trend and variability analysis of the past 119 (1901-2019) years using statistical techniques: A case study of Kolkata, India. Mausam 74(4) :1093–1112. Doi: 10.54302/mausam.v74i4.5909 Sarker M S H (2021) Regional spatial and temporal variability of rainfall, temperature over Bangladesh and Northern Bay of Bengal. Environmental Challenges 5.doi: 10.1016/j.envc.2021.100309 Shahid S (2010) Rainfall Variability and the Trends of Wet and Dry Periods in Bangladesh. International Journal of Climatology 30: 2299-2313. Singh A (2021) Statistical analysis of rainfall variability and temperature trends in Nepal: An application of linear and nonlinear approach. J Water Clim Change 12(8):2335–2347.doi: 10.2166/wcc.2021.374. Twahirwa A, Oludhe C, Omondi P, Rwanyiziri G, Sebaziga Ndakize J (2023) Assessing Variability and Trends of Rainfall and Temperature for the District of Musanze in Rwanda. Advances in Meteorology.doi: 10.1155/2023/7177776 Wan Zin W Z, Jamaludin S, Deni S M, Jemain A A (2010) Recent changes in extreme rainfall events in Peninsular Malaysia: 1971-2005. Theoretical and Applied Climatology 99(3–4):303–314. doi:10.1007/s00704-009-0141-x Additional Declarations No competing interests reported. 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07:23:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":102608,"visible":true,"origin":"","legend":"\u003cp\u003eWorkflow diagram for data processing methodology\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5098521/v1/39d1ddeb9d4b5267136dfbfa.png"},{"id":69059592,"identity":"5298ed99-1ca9-4936-855b-62f4b1ed59fa","added_by":"auto","created_at":"2024-11-15 07:15:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":372529,"visible":true,"origin":"","legend":"\u003cp\u003ea, b, c, d, e, f, g, h Decadal Variation in Annual Rainfall in Mymensingh division (1950-2020)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5098521/v1/1b9c9f9b3f501f3a8edd2715.png"},{"id":69059591,"identity":"3e2f108f-a5cd-4c8b-ad5d-1ca213d4b71d","added_by":"auto","created_at":"2024-11-15 07:15:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":59975,"visible":true,"origin":"","legend":"\u003cp\u003eSen's Slope analysis of long-term trends in Mymensingh\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5098521/v1/1f1cc189f939c948d6749a44.png"},{"id":69058667,"identity":"19e0c595-ac05-428f-bbe8-8aa8705e2713","added_by":"auto","created_at":"2024-11-15 07:07:50","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":48003,"visible":true,"origin":"","legend":"\u003cp\u003ea, b \u0026nbsp;Autocorrelation and Partial Autocorrelation of rainfall data.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5098521/v1/ddb8b5ea2004f818c66813bc.png"},{"id":69058669,"identity":"88b062d3-2ee5-428b-a3ee-53379e4ab169","added_by":"auto","created_at":"2024-11-15 07:07:50","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":237772,"visible":true,"origin":"","legend":"\u003cp\u003ea, b, c, d, e, f, g, h Annual temperature data covering per decade from 1950 to 2020.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-5098521/v1/8b263300db01d17e87c93614.png"},{"id":69058673,"identity":"6f109e55-f53f-40f4-8d4d-9bcadfb3adeb","added_by":"auto","created_at":"2024-11-15 07:07:50","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":167912,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual Avg. temperature ( ̊ C) from 1950 to 2020.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-5098521/v1/a81a32ffe5670e6a2468db84.png"},{"id":69059590,"identity":"cfc902af-1398-4ee2-b88a-1d945045e88c","added_by":"auto","created_at":"2024-11-15 07:15:50","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":118582,"visible":true,"origin":"","legend":"\u003cp\u003eStatical analysis for temperature from 1950 to 2020.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-5098521/v1/51f9c434acaca3872606583c.png"},{"id":69060516,"identity":"00849284-d8a8-4a7e-b1cf-c1115bcc04ec","added_by":"auto","created_at":"2024-11-15 07:31:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2018305,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5098521/v1/71128fbb-7372-43e4-b25d-ac8798b9f4d8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Reveals of a Shifting Climate: A Regional Analysis of Rainfall and Temperature Trends at Mymensingh Division in Bangladesh (1950-2020)","fulltext":[{"header":"Introduction","content":"\u003cp\u003eClimate change represents a significant global challenge, particularly affecting nations that are still developing. The parameters of temperature and precipitation are fundamental elements that shape ecological systems, with their fluctuations exerting a profound influence on climatic patterns. It is imperative to comprehend the long-term trajectories of these elements for the effective management of hydrological resources, strategic agricultural planning, and adaptation to the ramifications of climate change (Mohd Wani et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Alemayehu et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The formulation of effective adaptation strategies necessitates a thorough examination of long-term trends and extremes in regional climatic variables (Atube et al. 2022). In the Mymensingh Division of Bangladesh, the analysis of temperature trends over prolonged durations is essential, given the region's economic importance.Projections indicate that global surface temperatures may increase by 1.4 to 5.8\u0026deg;C between 1990 and 2100, with possible escalations of 1.5\u0026deg;C to over 2.0\u0026deg;C by the century's conclusion (Alam et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).A comprehensive understanding of these patterns is vital for promoting sustainable agricultural practices, effective disaster management, and climate adaptation strategies in Bangladesh. The impacts of climate change are heterogeneous, influenced by geographic and socioeconomic variables. Anticipated future climatic effects may heighten the frequency and intensity of floods and droughts, with temperature increases projected to range from 3 to 4\u0026deg;C (Esayas et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The variability in precipitation, influenced by both natural and anthropogenic factors, has significant implications for water resources, agricultural productivity, and ecosystems.Such variability complicates efforts in water management and mitigation strategies (Ahmad et al. 2022).Historical analyses of precipitation trends reveal increases in areas such as eastern North America and northern Europe, whereas declines have been recorded in the Sahel, Mediterranean region, and southern Asia (Odedra and Jadeja \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Rainfall patterns exhibit substantial temporal and spatial variability, driven by seasonal atmospheric alterations and geographic characteristics (Rani \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Detailed assessments of temperature and precipitation trends are vital for grasping and addressing the repercussions of climate change, in light of the interconnected characteristics of these phenomena and their global impacts.South Asia, including Bangladesh, experiences significant temperature variability, presenting challenges in identifying climate change and discerning its anthropogenic effects (Priyakumara and Ranasinghe \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Rising temperatures are projected to exacerbate challenges related to floods, droughts, and cyclones, with internal climatic fluctuations such as ENSO also contributing to flooding occurrences (Uddin et al. 2023). Comprehensive investigations of rainfall are crucial for effective water management and disaster preparedness. However, there exists a deficiency of localized studies addressing spatiotemporal rainfall variability within Bangladesh.Urban ecosystems deliver essential services, and their functionality is profoundly affected by climate change (Pandey and Ghosh \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Fluctuations in precipitation have a significant impact on food security, agricultural productivity, and the associated risks of droughts and floods.A report by the Asian Development Bank and the International Food Policy Research Institute (2009) identified Bangladesh as extremely susceptible to climate change, based on indicators related to alterations in temperature and precipitation, agricultural labor dynamics, and poverty levels (Billah et al. 2015). The challenge of addressing climate change necessitates localized analyses to facilitate informed decision-making and the development of effective adaptation strategies (Dadi et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).Alleviating the effects of climate change requires a cutback on local emissions, the implementation of greenhouse gas reduction strategies, and the enhancement of infrastructure resilience. Comprehension of regional climate variability and transitions is imperative for enhancing climate projections and formulating effective adaptation strategies. Employing statistical methodologies for the analysis of trends and variability in temperature and precipitation data is fundamental to this endeavor (Manglem \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Investigative research into temperature and precipitation fluctuations is crucial for urban areas that are experiencing the impacts of climate change (Balogun et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This study is centered on the Mymensingh Division, scrutinizing long-term trends in temperature and precipitation from the year 1950 to 2020. It examines temperature patterns, analyzes the correlation between temperature and precipitation variability, and offers insights to inform policy and adaptation strategies in response to a dynamically changing climate.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Area\u003c/h2\u003e \u003cp\u003eMymensingh division is located in northern Bangladesh and is bordered by Meghalaya (India) to the north, the district Gazipur to the south, Netrokona and Kishoreganj districts to the east, and Sherpur, Jamalpur, and Tangail districts to the west. Mymensingh division is a metropolitan city of Bangladesh located on the bank of the old Brahmaputra River about 120 km north of the national capital city Dhaka. It lies on 24°45′14″N 90°24′11″E. Mymensingh Division covers an area of about 10,485 (kilometers)\u003csup\u003e2\u003c/sup\u003e and it has a population of about 12,225,498. The division Mymensingh’s climate is closer to the Himalayan Mountain that’s why their temperature is a little colder than the capital city Dhaka's temperature. Mymensingh occupies a Tropical wet and dry or savanna climate. The maximum amount of rainfall occurs during the monsoon season (May/June to August). The area falls under monsoon climatic repossess and annual temperature differs from 13ºC to 37ºC average monthly rainfall: January 12mm, June 469mm, December 2mm, and most the time total annual rainfall varies from 2000mm to 2500mm.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData sources\u003c/h2\u003e \u003cp\u003eCRU TS (Climatic Research Unit gridded Time Series) is a broadly used climate dataset on a 0.5° latitude by 0.5° longitude grid over all land domains over the world except Antarctica. It is extracted by the interpolation of monthly climate irregularities from extensive networks of weather station observations (Harris et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData processing\u003c/h2\u003e \u003cp\u003eThe principle of the methodology lies in the application of certain ArcGIS tools. The \"Make Net CDF Raster Layer\" tool is used to create a raster layer from Net CDF files, while \"Raster Processing\" tools make possible the manipulation, editing, and management of raster data. To analyze cell values across multiple raster, the \"Cell Statistics\" tool is employed to derive statistical information. Besides, the \"Inverse Distance Weighted (IDW)\" tool comes into play for interpolating raster surfaces from point data using a reverse distance weighted technique. Following the application of these tools, the processed data is exposed to analysis and visualization, leveraging ArcGIS mapping and visualization capabilities to present the results in a relevant manner.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eRainfall Pattern Analysis with Non-parametric Statistical Techniques\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003eMenn- Kendal Analysis: Rainfall (mm)\u003c/h2\u003e \u003cp\u003eThe Mann-Kendall test, also referred to as Kendall's statistic, is a non-parametric experiment that is mainly used to repress randomness in trends in climatology and hydrology (Odedra and Jadeja \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Mann-Kendall statistics ‘S’ is given as:\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eS = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sum\\:_{i=1}^{n-1}{\\sum\\:}_{j=i+1}^{n}Sgn\\:(xj-xi)\\)\u003c/span\u003e\u003c/span\u003e\u003c/h2\u003e \u003cp\u003eWhen statistic ‘S’ is positive, the data is drifting upward, and when it is negative, the data is trending lower. The variance of the Mann-Kendall statistics for sample sizes n ≥ 8 is given by\u003c/p\u003e \u003cp\u003eVar (S) = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{[n\\left(n-1\\right)\\left(2n+5\\right)-{\\varSigma\\:}_{t}\\left(t-1\\right)\\left(2t+5\\right)]\\:}{18}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eWhere ‘t’ is referred to as the extent of any tie.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eSen’s Slope\u003c/h2\u003e \u003cp\u003eA straightforward non-parametric method created by Sen in 1968 can be used to examine the true slope of a time series if it has a linear trend.\u003c/p\u003e \u003cp\u003eIn the sample of N data pairs, the trend line's slope is to be calculated by\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eQ = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{xj-xi}{j-i}\\)\u003c/span\u003e\u003c/span\u003e\u003c/h2\u003e \u003cp\u003eWhere x\u003csub\u003ej\u003c/sub\u003e and x\u003csub\u003ei\u003c/sub\u003e are the data values at times j and i (j \u0026gt; i) respectively.\u003c/p\u003e \u003cp\u003eThe median of these ‘N’ values of Q is Sen’s estimator of slope which is calculated as\u003c/p\u003e \u003cp\u003e𝛃 = Q \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{N+1}{2}\\)\u003c/span\u003e\u003c/span\u003e ; if N is odd.\u003c/p\u003e \u003cp\u003e𝛃 = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{Q\\left(\\frac{N}{2}\\right)+Q\\left(\\frac{N+2}{2}\\right)}{2}\\)\u003c/span\u003e\u003c/span\u003e ; if N is Even.\u003c/p\u003e \u003cp\u003eUltimately, β is calculated using a two-sided experiment with a 100 (1-α) % confidence interval, and the non-parametric test can then be used to find out the true slope. A rising or rising trend in a specific time series is shown by a positive value of β, and a falling or decreasing trend is shown by a negative value of β.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAuto-correlation Tests\u003c/h2\u003e \u003cp\u003eIt is mandatory to find out autocorrelation in the time series of the data before performing a trend test on rainfall data. A rough calculation of the population autocorrelation coefficient is the sample autocorrelation coefficient (Saikh et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Both the successive coefficient and the lag-1 autocorrelation coefficient can be calculated (Piyoosh and Ghosh 2016). However, a worldly most used indicator of time series dependency is the first serial correlation coefficient to ascertain the existence of serial independence (Basistha et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Mondal et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). An analysis is supervised by contrasting the possible hypothesis with the null hypothesis. The alternative hypothesis is accepted if there is a notable rejection of the null hypothesis. To investigate this, a significance threshold of 5% is considered suitable.\u003c/p\u003e \u003c/div\u003e "},{"header":"Result and Discussion","content":"\u003ch2\u003eRainfall Analysis\u003c/h2\u003e\u003ch2\u003eDescriptive Analysis of Rainfall Data\u003c/h2\u003e\u003cp\u003eThe rainfall data under analysis comprises 96 observations, all of which are complete with no missing values. The descriptive statistics, as shown in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, indicate that the minimum recorded annual rainfall was 1752 mm, while the maximum reached 4338 mm. The mean annual rainfall was calculated at 2760.52 mm, with a standard deviation of 551.27 mm, indicating variability in rainfall patterns across the years. The relatively high standard deviation suggests that there is considerable fluctuation in annual rainfall, which is critical for understanding climate dynamics and water resource management in the region.\u003c/p\u003e\u003cdiv class=\"gridtable\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive Statistics of Rainfall Data\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eObs. with missing data\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eObs. without missing data\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eStandard Deviation\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRainfall (mm)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1752.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4338.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2760.521\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e551.269\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003ch2\u003eDecadal Variation in Rainfall\u003c/h2\u003e\u003cp\u003eIn Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e(a), which shows data from 1951 to 1960, the greatest recorded rainfall was between 2025 mm and 2285 mm, while the lowest amounts were between 3060 mm and 3317 mm. During this time, Bangladesh's average annual rainfall was roughly 2200 mm. Rainfall patterns from 1951 to 1960 are depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e(b), with most values similar to the national average, though certain areas experienced above-normal rainfall. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e(c) illustrates rainfall data from 1961 to 1970, revealing lower-than-average rainfall with values ranging from 1988 mm to 2380 mm. In contrast, the 1980s, as seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e(d), had near-average rainfall, with peak values ranging from 1952 mm to 2208 mm. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e(e) displays the decade of 1981–1990, when maximum rainfall ranged from 2135 mm to 2418 mm, closely reflecting regional temperature fluctuations. The rainfall from 1991 to 2000, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e(f), was comparable with the national average. The highest recorded rainfall from 2001 to 2010, as indicated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e(g), was between 1829 and 2103 mm. Finally, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e(h) shows the period from 2011 to 2020 when the minimum recorded rainfall was between 3360 mm and 3630 mm, which was higher than the national average. Rainfall trends from 1951 to 2010 show oscillations, with values near the national average from 1951 to 1960, below-average rainfall between 1988 mm and 2380 mm in 1961–1970, near-average levels peaking at 2208 mm in the 1980s, and rainfall ranging from 1829 mm to 2418 mm between 1981 and 2010 (Tanvir 2019).\u003c/p\u003e\u003ch2\u003eMann-Kendall Trend Test\u003c/h2\u003e\u003cp\u003eMann-Kendall trend test / 2-tailed test (mm) ;\u003c/p\u003e\u003cp\u003eWhere,\u003c/p\u003e\u003cp\u003eKendall’s tau = 0.156\u003c/p\u003e\u003ch2\u003eS = 713\u003c/h2\u003e\u003cp\u003eVar(S) = 99812.333\u003c/p\u003e\u003cp\u003eP -value (two-tailed) = 0.024\u003c/p\u003e\u003cp\u003eAlpha (α) = 0.05\u003c/p\u003e\u003cp\u003eAn approximation has been used to compute the p-value.\u003c/p\u003e\u003ch2\u003eTest Interpretation\u003c/h2\u003e\u003cp\u003e \u003cstrong\u003eHypothesis\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eH0: No trend exists in the series.\u003c/p\u003e\u003cp\u003eHa: A trend exists in the series.\u003c/p\u003e\u003cp\u003eThe p-value (0.024) is below the significance level (α = 0.05), so we reject H0 and accept Ha. The study involved continuity correction and tie corrections. The Mann-Kendall trend test found a substantial rising trend in yearly rainfall (Kendall's tau = 0.156). This finding suggests a good trend in rainfall during the research period(Mondal et al. 2012) found a similar positive trend in northeastern Bangladesh with a Kendall's tau of 0.18, while Rahman et al. (2017) found a somewhat weaker trend (Kendall's tau = 0.12) in their study. Our findings suggest a slightly stronger trend, which may represent changing climatic circumstances caused by global climate change. This supports earlier research and has importantimplications for water resource management, agriculture, and flood risk. The coherence with current research emphasizes the importance of continuous monitoring to understand and address the effects of climate variability. More research is needed to understand the causes of these changes and their long-term repercussions.\u003c/p\u003e\u003ch2\u003eSen’s Slope Estimation\u003c/h2\u003e\u003cp\u003eThe Sen's Slope analysis as presented in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, indicates a positive trend in annual rainfall with an average increase of 1.9 mm per year over the study period from 1950 to 2020. This analysis which considers variability across different decades, suggests a significant shift in rainfall patterns in the region.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\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\u003eConfidence interval for Sen’s Slope estimation of Rainfall Trends\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLower Bound (95%)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUpper Bound (95%)\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.900\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.920\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.433\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1021.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-10512.167\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-60.600\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eSen's slope analysis of annual rainfall from 1950 to 2020 indicates an average increase of 1.9 mm/year. Sensitivity analysis, depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, involves creating a rainfall model and systematically varying input parameters, such as meteorological conditions and land use, to assess their impact on rainfall outputs. This approach, using methods like factorial design or variance-based techniques, helps evaluate model robustness, precision in forecasting, and the uncertainty of rainfall projections. The observed Sen's slope of 1.9 mm/year in this study contrasts with a lower 1.2 mm/year found in other studies across Bangladesh (Ahmed et al. 2019) and is closer to a 2.1 mm/year increase reported in northern Bangladesh (Hossain et al. 2020), highlighting regional variations in rainfall trends.\u003c/p\u003e\u003cp\u003e \u003cb\u003eTable\u0026nbsp;3 Data Table for Autocorrelation and partial correlation for rainfall trends from 1950 to 2020.\u003c/b\u003e \u003c/p\u003e\u003cp\u003e\u003cimg 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\" width=\"497\" height=\"358\"\u003e\u003c/p\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e(a): The autocorrelogram shows the temporal correlation of rainfall data for Mymensingh Division from 1950 to 2020. The x-axis represents time lags in years, and the y-axis displays the correlation coefficient, indicating the strength and direction of relationships between observations at different lags. Strong positive correlations at certain lags suggest persistent patterns, hinting at seasonal cycles or long-term trends, while weak correlations indicate randomness (Ahmed et al. 2019). Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e(b): The partial autocorrelogram controls for intermediate observations to highlight the direct effects of historical rainfall measurements on current values. This method isolates significant temporal dependencies, offering clearer insights into the key lags influencing rainfall patterns in the region.\u003c/p\u003e\u003ch2\u003eTemperature Analysis\u003c/h2\u003e\u003ch2\u003eDecadal Analysis of Temperature Data\u003c/h2\u003e\u003cp\u003eThe analysis of annual temperature data for Mymensingh Division from 1950 to 2020, given in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, demonstrates significant decade-wise variability. From 1950 to 1960, maximum temperatures were between 24.77°C and 25.06°C, while minimum temperatures ranged between 23.55°C and 23.85°C, which was lower than the area average of 27.05°C. In Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e(b), greater temperatures were reported from 1961 to 1970, with maximum values ranging from 25.77°C to 26.08°C. The 1970s, represented in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e(c), exhibited a consistent range of 25.52°C to 25.62°C. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e(d) depicts a little cooling in the 1980s, with maximum temperatures ranging from 24.99°C to 25.26°C and minimums ranging from 23.85°C to 24.13°C. Temperatures ranged between 24.81°C and 25.11°C from 1991 to 2000, according to Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e(e). Temperatures throughout the 2000s, as indicated in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e(f), ranged between 24.77°C and 25.08°C. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e(g) shows that temperatures increased between 2011 and 2020, ranging from 24.89°C to 25.17°C, mirroring recent warming trends.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of Rainfall Trends in Mymensingh Division (1950–2020)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMin Rainfall (mm)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMax Rainfall (mm)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean Rainfall (mm)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStd Deviation\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1950\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1766\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3317\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2542.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e441.79\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1960\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3382\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2700.25\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e388.24\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1970\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1988\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4338\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2772.67\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e484.71\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1980\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1752\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3121\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2216.83\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e497.42\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1990\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2135\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3837\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2703.67\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e468.60\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1908\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3655\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2491.67\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e461.36\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2010\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1829\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3475\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2377.17\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e497.42\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2009\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3630\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2279.67\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e468.60\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e depicts annual rainfall variations in Mymensingh Division from 1950 to 2020, with significant oscillations in minimum (1752 mm in the 1980s) and maximum (4338 mm in the 1970s) rainfall, as well as changes in standard deviation. These changes reflect times of extreme wetness and dryness, which is consistent with previous research on Bangladesh's rainfall trends. The standard deviation demonstrates variability, with some decades having more constant rainfall than others. Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows the mean temperatures for the same time period, which ranged from 23.55°C to 26.65°C. Temperatures remained steady throughout the 1950s and 1960s, but a gradual warming began in the late 1960s. After a minor decline in the 1980s, temperatures surged again, peaking between 2010 and 2020.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTemperature Patterns and Statistical Summary for Mymensingh\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMinimum Temperature\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMaximum Temperature\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean Temperature\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStandard Deviation\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1950\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23.5519\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.3581\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.7017\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3806\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1960\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24.1529\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.0833\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.6494\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2971\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1970\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25.0523\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.7333\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.49\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1301\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1980\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.42\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.22\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.93\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.6092\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1990\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23.85\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.54\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.94\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3623\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23.5689\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.4161\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.7404\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3852\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2010\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24.7679\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.6502\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.9947\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3964\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23.7174\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.4591\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.8371\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3727\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eThe regional temperature statistics for Mymensingh Division from 1950 to 2020, displayed in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e, reveal significant changes in mean, minimum, and maximum temperatures, with varied standard deviations. These changes represent the region's changing climate during the last seventy years. The data show significant fluctuations in temperature extremes, which are likely impacted by broader climatic variables. From 1950 to 1960, the mean temperature rose while both minimum and maximum temperatures increased. This rising trend continued throughout the 1970s, but the standard deviation fell, indicating less fluctuation in temperature extremes. The 1980s experienced a decrease in both minimum and maximum temperatures, while the mean temperature remained consistent, albeit with more variability as indicated by a larger standard deviation. Temperatures fluctuated during the 1990s, with a minor rise in mean temperature and considerable fluctuations in minimum and maximum temperatures. Temperatures fell slightly throughout the 2000s, but the latter half of the decade and the 2010s saw a dramatic increase in both minimum and maximum temperatures, in accordance with worldwide warming trends. The observed increase in mean temperature, combined with rising yearly rainfall, supports a more variable climate, echoing previous studies linking temperature increases to rainfall variability. This study's temperature rise of 0.02°C/year exceeds Bangladesh's recorded increase of 0.015°C/year. These developments are consistent with broader patterns in South Asia, where global warming is causing increased temperature and rainfall variability.\u003c/p\u003e\u003ch2\u003eTemperature Trends and Rainfall Variability Effects\u003c/h2\u003e\u003cp\u003eThe climatic conditions observed in the Mymensingh Division from the year 1950 to 2022 exhibited considerable fluctuations in both precipitation and temperature. In the decade of the 1950s, the annual rainfall experienced a substantial increase from 1766 mm to 3317 mm by the 1960s, ultimately reaching a peak of 3655 mm in the 2000s before stabilizing at 3630 mm in the 2020s. Rainfall levels remained relatively stable throughout the decades of the 1970s and 1980s, characterized by only minor fluctuations. The temperature, which experienced a slight increase during the 1950s and 1960s, witnessed a significant escalation in the 1980s, attaining its zenith in the late 1980s. This upward trajectory in temperature persisted throughout the 1990s and 2000s, coinciding with an increase in precipitation levels. In the most recent decades, the trends pertaining to both temperature and rainfall have continued to ascend, indicating a potential correlation. Decadal and autocorrelation analyses have unveiled noteworthy alterations in climatic patterns, underscoring the variability of precipitation (Shahid \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and the periodic fluctuations in temperature (Hossain et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These findings are consistent with prior research on climate variability.\u003c/p\u003e\u003ch2\u003eExtreme Climatic Events Occurred in Mymensingh Division from 1950 to 2020\u003c/h2\u003e\u003cp\u003eThe exploration of severe weather phenomena in Mymensingh Division (1950–2020) uncovers intriguing links between escalating temperatures and the surge in rainfall. Particularly, the late 1980s marked a period where temperature and precipitation soared in tandem, suggesting intricate climate dynamics (Karmakar and Shrestha \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). During the decade spanning the 1950s to the 1960s, annual rainfall dramatically escalated from 1766 mm to an impressive 3317 mm, with temperatures also climbing steadily. By the twilight of the 2000s, annual rainfall soared to 3655 mm, a trend that persisted into the 2010s and 2020s (Shahid \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The 1970s and 1980s exhibited consistent rainfall, yet temperatures reached their zenith in the late 1980s.These observed trends resonate with the broader narrative of global climate change, underscoring the heightened occurrence of extreme weather phenomena in Bangladesh. Mymensingh's susceptibility to these climatic alterations accentuates the urgent requirement for effective water management and resilient infrastructure.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eRainfall and temperature statistics for Mymensingh Division from 1950 to 2020 show major climatic shifts with far-reaching ramifications for water supplies, agriculture, and ecosystems. The results show a significant increase in both temperatures and annual rainfall. The Mann-Kendall trend test finds a Kendall's tau of 0.156 with a p-value of 0.024, while Sen's Slope analysis shows a 1.9 mm annual rise in rainfall (Ahmad et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Akter et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This rainfall variability, which encompasses both extreme wet and dry spells, highlights the region's increased sensitivity to changing climate patterns. This observation is consistent with global trends (Harris et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Alam et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The higher trend in temperatures found in this study is consistent with global warming predictions, highlighting the crucial need for effective ways to reduce and adapt to these climatic changes (Rahman \u0026amp; Lateh, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Karmakar \u0026amp; Shrestha, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Shahid, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The increase in temperature and rainfall creates a complicated combination of difficulties for water supply reliability, agricultural production, and ecosystem health. Addressing these difficulties necessitates a comprehensive strategy that includes both mitigation and adaptation measures. A thorough understanding of regional climate dynamics is critical for formulating evidence-based policy and effective response strategies. Continued study and observation are required to improve our understanding of how climate change affects local ecosystems and human systems. This study gives unique insights into the climatic shifts affecting Mymensingh Division, emphasizing the need of incorporating these findings into policy and planning. Using this knowledge, stakeholders can develop tailored interventions to address specific risks, encourage sustainable behaviors, and boost resilience across many sectors. Furthermore, preventive interventions based on reliable data might help predict future issues and mitigate potential dangers linked with ongoing climate variability. The integration of scientific discoveries into practical solutions will be critical for navigating the changing climate scenario and ensuring the region's long-term sustainability.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis is not relevant to the current study\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis is not relevant to the current study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets employed in this investigation are sourced from publicly accessible repositories, namely the CRU TS (Climatic Research Unit gridded Time Series) dataset. The CRU TS dataset is readily available through the Climatic Research Unit's archive at the University of East Anglia and can be retrieved at https://crudata.uea.ac.uk/cru/data/hrg/. Supplementary processed data and materials utilized throughout the present study can be obtained from the corresponding author upon reasonable inquiry.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have disclosed that there are no potential conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received no outside funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe manuscript was carefully crafted through the collaborative efforts of all listed authors. KH Haque took on the main responsibility for writing the initial draft and conducting the data analysis, ensuring that the research findings were presented with both rigor and accuracy. RK Hassan, who serves as an Assistant Professor in the department, made significant contributions to the review and editorial processes, thus ensuring that the manuscript met high academic standards. All authors worked closely together throughout the research project and have approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are deeply grateful to Md. Aminul Islam for his meticulous editing and evaluation, both of which significantly raised the manuscript's caliber. We also thank Md. Fazle Rabbi for his assistance with data reorganization and insightful feedback during the development of this study. We are grateful to the University of East Anglia for supplying high-resolution gridded datasets of historical temperature and rainfall data, which were critical to the study's success.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions of Authors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe manuscript was developed through the collaborative efforts of all listed authors. KH Haque was responsible for writing the original draft and conducting the data analysis, ensuring that the research findings were thoroughly and accurately presented. RK Hassan, who is an Assistant Professor in the department, contributed significantly to the review and editing process, ensuring the manuscript met high academic standards. All authors worked closely together throughout the research process and have approved the final version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAhmad K, Banerjee A, Rashid W, Xia Z, Karim S, Asif M (2023) Assessment of Long-Term Rainfall Variability and Trends Using Observed and Satellite Data in Central Punjab, Pakistan. Atmosphere 14(1).doi: 10.3390/atmos14010060\u003c/li\u003e\n\u003cli\u003eAhmed T, Jin H G, Baik J J (2020) Spatiotemporal Variations of Precipitation in Bangladesh Revealed by Nationwide Rain Gauge Data. Asia-Pacific Journal of Atmospheric Sciences 56(4):593\u0026ndash;602. doi: 10.1007/s13143-019-00168-z\u003c/li\u003e\n\u003cli\u003eAkinsanola A, O K (2014) Analysis of Rainfall and Temperature Variability Over Nigeria. 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Advances in Meteorology.doi: 10.1155/2023/7177776\u003c/li\u003e\n\u003cli\u003eWan Zin W Z, Jamaludin S, Deni S M, Jemain A A (2010) Recent changes in extreme rainfall events in Peninsular Malaysia: 1971-2005. Theoretical and Applied Climatology 99(3\u0026ndash;4):303\u0026ndash;314. doi:10.1007/s00704-009-0141-x\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":"environmental-systems-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ensr","sideBox":"Learn more about [Environmental Systems Research](http://environmentalsystemsresearch.springeropen.com)","snPcode":"40068","submissionUrl":"https://submission.nature.com/new-submission/40068/3","title":"Environmental Systems Research","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Climate change, IDW, Non-Parametric Test, Annual temp. and rainfall trends","lastPublishedDoi":"10.21203/rs.3.rs-5098521/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5098521/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eProfessional resource allocation and planning in response to climate change in developing regions such as the Mymensingh Division of Bangladesh requires comprehension of trends in temperature and precipitation over long periods of forecast. This is the reason why this study examines the temperature and precipitation from the years 1950 to 2020 in order to provide a reasonable view of local climatic conditions and facilitate the policymaking process. By using climactic research unit (CRU) TS data sets in creation of raster layers using ArcGIS tools we undertook data processing research which involved statistical analysis methods. Mann-Kendall test has generated a very encouraging result as it has found relative increase in annual precipitation, averaging about 2760.52 mm and oscillating between 1752 mm and 4338 mm. Kendall\u0026rsquo;s tau correlation τ\u0026thinsp;=\u0026thinsp;0.156, p-value\u0026thinsp;=\u0026thinsp;0.024, shows a possible change over a period of time. Slope of sen demonstrated that precipitation regime has increase by 1.9 mm annually. The analyses of autocorrelation and partial autocorrelation confirmed that the precipitation data upside and trends are clearly delineated. Progressive warming trend as regards the average annual temperature was observed, as the years went by, the average annual temperature increased from 24.77 0 c to 25.170c, more so in recent years where there have been high degree of warming. This study highlights the need for ongoing climate and the enhancement of global warming policies to prevent worsening situations.\u003c/p\u003e","manuscriptTitle":"Reveals of a Shifting Climate: A Regional Analysis of Rainfall and Temperature Trends at Mymensingh Division in Bangladesh (1950-2020)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-15 07:07:45","doi":"10.21203/rs.3.rs-5098521/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-28T03:15:06+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-27T19:49:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-27T03:59:51+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-26T10:50:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-26T10:03:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-21T19:45:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"217467913689511656560093409206778196144","date":"2024-09-21T19:03:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"237537973667657491229722263891176033660","date":"2024-09-20T06:41:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"112095555880840411699684037546195470744","date":"2024-09-20T04:16:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"192558134171260333835111552096356547623","date":"2024-09-20T02:20:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"166591984905701055315968894838651306378","date":"2024-09-20T01:46:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"138201439273685708998155767234237520081","date":"2024-09-19T06:12:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"197125884193319111967583774089207987803","date":"2024-09-18T19:19:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"171033397173094372566195276221199134711","date":"2024-09-18T05:18:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"54614525681955525680336599410037115460","date":"2024-09-17T16:53:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"294655528186380459531250176128172953898","date":"2024-09-17T15:06:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"54454051094041249849344719890154629607","date":"2024-09-17T14:45:23+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-09-17T14:23:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-09-17T14:19:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-09-17T07:03:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Systems Research","date":"2024-09-16T15:45:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-systems-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ensr","sideBox":"Learn more about [Environmental Systems Research](http://environmentalsystemsresearch.springeropen.com)","snPcode":"40068","submissionUrl":"https://submission.nature.com/new-submission/40068/3","title":"Environmental Systems Research","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cc24c640-8d92-4203-958d-649109e65cdd","owner":[],"postedDate":"November 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-12-13T13:38:35+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-15 07:07:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5098521","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5098521","identity":"rs-5098521","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}