Understanding Spatio-Temporal Dynamics of Extreme Rainfall and Temperature Events over state of Bihar, India

Understanding Spatio-Temporal Dynamics of Extreme Rainfall and Temperature Events over state of Bihar, India | 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 Understanding Spatio-Temporal Dynamics of Extreme Rainfall and Temperature Events over state of Bihar, India Neha Pareek, Manjari Singh, Hemant Kumar, Arvind Kumar Singh, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7731739/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The present study examines changes in extreme temperature and rainfall patterns across Bihar’s districts over a span of four decades (1982–2022), focusing on their spatial and temporal dynamics. Daily weather data for parameters such as maximum temperature, minimum temperature, and rainfall were sourced from the NASA-POWER database ( https://power.larc.nasa.gov ) to compute climate extreme indices. The formulation of these indices adhered to the standardized approaches suggested by the ETCCDI and ET-SCI for consistent climate change and sector-specific climate analysis. Notably, this is the foremost comprehensive analysis focusing on the temporal trends of climate extremes at the district level in Bihar. Bihar's climate trends spanning four decades exhibit notable transformations in meteorological variables. Reduced summer days hint at a cooling trend, while lengthen tropical nights reflect nocturnal warming. Although warm days decrease uniformly, warm nights surge in most districts. Diverse patterns emerge in monthly maximum temperatures (TXx), with some south-western districts bucking the trend. Rising minimum temperatures are evident except in Katihar and Jamui. Crucially, heavy rainfall dynamics are pivotal, with escalating events varying by time frame and locale. Eastern districts face increased short and medium-term heavy precipitation risks, while the central and western regions encounter prolonged heavy rainfall episodes over five days (Rx5day). Changing dry and wet day frequencies impact water resources and agriculture, accentuating the need for tailored adaptation strategies. Bihar's climatic complexity underscores regional disparities, emphasizing the urgency of responsive environmental awareness and strategies. ETCCDI ET-SCI summer days tropical night Bihar Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. INTRODUCTION Addressing climate change is now a major priority on the global agenda, leading to alterations in the Earth's climatic conditions and posing severe threats to ecosystems, human livelihoods, and socio-economic development (IPCC, 2023; Abbass et al., 2022 ; IPCC 2014 ; Gupta, A. and Pathak, H. 2016 ). In recent years, the consequences of climate change have become more apparent, showing through severe climatic disturbances, rising temperatures, and erratic precipitation trends (Fallah et al , 2024; Wegler et al , 2024; Chisale, 2021; WMO, 2020). Among the various regions affected by these climatic shifts, the Indian subcontinent has experienced substantial variations, leading to pronounced impacts on its agriculture, water resources, and overall environment (Government of India, 2018 ; Balasubramanian, M., and Birundha, V.D. 2012). The state of Bihar, situated in the eastern part of India, bears witness to the multifaceted implications of climate change on its environment and society (Gupta et al., 2022 ; Government of Bihar, 2021 ; Tesfaye et al 2017 ; Roy et al 2016 ; Singh et al 2014 ). The region's predominantly agricultural economy and large population are predominantly vulnerable to extreme weather events, making it essential to comprehend the spatio-temporal dynamics of precipitation and temperature extremes in this area (Jain, et al., 2013 ; Warwade, et al, 2018 ). The present investigation is intended to conduct an in-depth examination of these extremes to gain deeper insights into their patterns, trends, and potential implications for Bihar. Numerous studies have focused on investigating the variations of these indices, either related to temperature, precipitation, or both, within the region (Kholodovsky et al ., 2021; Rashiq et al., 2021 ). Such investigations yield critical understanding of evolving climate trends, facilitating deeper insight into the inferences of climate change for the state's environment and communities. Furthermore, some researchers have analyzed the Spatial patterns of temperature and precipitation-based extreme indices across different districts and river basins within Bihar (Chhabra and Haris, 2015). These localized studies offer critical information on how climate change affects specific regions within the state, aiding in the formulating targeted adaptation and mitigation approaches (Sen, Z. 2012 ). Moreover, to assess potential impacts in Bihar, several studies have explored how temperature and rainfall indices may behave across various climate scenarios (Rajput et al., 2023 and Zakwan et al ., 2019). These future projections help policymakers and stakeholders in understanding potential climate risks and taking proactive measures to build resilience (Gellens et al ., 2000; Sen, Z., 2012 ; Jain et al., 2013 ). In addition to studying these indices within the Bihar region, some researchers have also conducted broader regional or global assessments of extreme weather events (Gajbhiye et al., 2016 ; Mondal et al., 2015 Singh et al., 2022 ; Singh et al., 2021 , Sharma et al ., 2023). These comprehensive studies provide valuable context to understand how Bihar's climate trends fit into the larger climate change narrative. In nutshell, research concerning temperature and rainfall extremes across Bihar has been crucial in understanding the changing climate patterns and their potential impacts on the region's environment, agriculture, and society. These studies offer valuable insights that can inform evidence-based policies and strategies to safeguard Bihar's communities, agriculture and ecosystems from the challenges posed by climate change. 2. MATERIALS AND METHODS 2.1 Study area Bihar, a state in eastern India, is situated between latitudinal range of 24°20′ to 27°31′ north and longitude 83°19′ to 88°17′ east, encompassing a considerable latitudinal (approximately 3.5 degrees) and longitudinal (around 5 degrees) span. The state's unique geographic location is characterized by diverse surroundings, including the Ganges River to the south, the Tarai region and the Himalayan foothills to the north, and the borders with Nepal in the east and Uttar Pradesh in the west. Such diverse topography and geographical features contribute to Bihar experiencing varying climatic conditions. Due to its geographical positioning, Bihar encounters a mix of tropical, sub-tropical, and temperate climate types, with distinctive variations in weather patterns across its regions. The mean maximum temperature exhibits considerable variability throughout the state, influenced by factors such as altitude, proximity to water bodies, and seasonal changes. The summer months witness higher temperatures, while winters can be relatively cooler, especially in the northern regions. Bihar receives majority of precipitation during monsoon season. The region’s rainfall is predominantly influenced by the southwest monsoon, which stays active from June to September and delivers most of the state’s annual precipitation. In the state diverse precipitation pattern contributes to a wide range of ecological settings and supports a rich and varied array of flora and fauna across Bihar. 2.2 Data collection The study utilized daily point-scale meteorological data for all districts of Bihar, retrieved from the NASA POWER project, which operates under NASA’s Earth Science Research Program ( https://power.larc.nasa.gov/data-access-viewer/ ). The NASA POWER dataset offers solar and meteorological variables at a global spatial resolution of 0.5° × 0.5°, available from 1981 onward. This analysis included daily temperature extremes (maximum and minimum) and rainfall as key parameters, collected for 38 locations across Bihar for the period 1982–2022. The inclusion of maximum and minimum temperature data along with rainfall measurements provides a comprehensive picture of the climatic conditions in Bihar over this extensive time span. This dataset is invaluable for conducting detailed analyses and drawing insightful conclusions regarding the long-term weather patterns and trends within the region. The utilization of NASA POWER's spatially-resolved and temporally-extended dataset enhances the accuracy and reliability of the research findings, contributing to a deeper understanding of Bihar's climatic dynamics. 2.3 Extreme climate indices To assess climate variability, this study utilizes 24 standardized indices recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI) and the Expert Team on Sector-Specific Climate Indices (ET-SCI) to examine trends in temperature and precipitation extremes, as detailed in Table-1. Climpact v2 software was employed to derive the climate indices using daily precipitation and temperature (maximum and minimum) data. Prior to computing these extreme indices, a thorough data quality control process was conducted on the dataset. Comprehensive instructions for utilizing Climpact v2, whether with a single station dataset or multiple stations, can be accessed at climpact/www/user_guide/Climpact_user_guide.md at master· ARCCSS-extremes/climpact · GitHub. Table-1 outlines the classification of climate extremes into four types: intensity-based, duration-based, absolute threshold-based, and relative threshold-based indices. This investigation seeks to shed light on the trends related to these indices, offering insights into how temperature and precipitation extremes have evolved over time. The Climpact v2 software's capabilities enhance the precision and credibility of the analysis, contributing to a more nuanced understanding of the occurrence and characteristics of extreme climatic conditions. Table-1 S.N. Index Name Description ET Unit 1 CDD Consecutive dry days Maximum number of consecutive days with RR < 1mm (RR-Rainfall) ETCCDI days 2 CWD Consecutive wet days Maximum length of wet spell, maximum number of consecutive days with RR ≥ 1mm ETCCDI days 3 PRCPTOT Total precipitation in wet days Annual total precipitation in wet days ETCCDI mm 4 R10mm Heavy precipitation days Annual count of days when PRCP≥ 10mm ETCCDI days 5 R20mm Very heavy precipitation days Annual count of days when PRCP≥ 20mm ETCCDI days 6 R30mm Extreme precipitation days Annual count of days when PRCP≥ 30mm ETCCDI days 7 R95pTOT Contribution from very wet days Annual total PRCP when RR > 95p ET-SCI % 8 R99pTOT Contribution from extremely wet days Annual total PRCP when RR > 99p ET-SCI % 9 Rx1day Max 1-day precipitation Monthly maximum 1-day precipitation ETCCDI mm 10 Rx3day Max 3-day precipitation Monthly maximum consecutive 3-day precipitation ETCCDI mm 11 Rx5day Max 5-day precipitation Monthly maximum consecutive 5-day precipitation ETCCDI mm 12 SDII Simple daily intensity index Simple precipitation intensity index ETCCDI mm 13 SU25 Summer days Annual count of days when TX (daily maximum temperature) > 25 o C ETCCDI days 1 TN10p Cold nights Percentage of days when TN 90 th percentile ETCCDI % days 16 TNn Min TN Monthly minimum value of daily minimum temperature ETCCDI °C 17 TNx Max TN Monthly maximum value of daily minimum temperature ETCCDI °C 18 TR20 Tropical nights Annual count of days when TN (daily minimum temperature) > 20 o C ETCCDI days 19 TX10p Cold days Percentage of days when TX 90 th percentile ETCCDI % days 21 TXge30 TX of at least 30 °C Annual number of days with TX of at least 30 °C ET-SCI days 22 TXge35 TX of at least 35 °C Annual number of days with TX of at least 35 °C ET-SCI days 23 TXn Min TX Monthly minimum value of daily maximum temperature ETCCDI °C 24 TXx Max TX Monthly maximum value of daily maximum temperature ETCCDI °C Reference: https://climpact-sci.org/indices 3. RESULTS AND DISCUSSION 3.1 Temperature thresholds Fig. (a) and (b) represents trend of SU25 and TR20 in different districts of Bihar over a 40-year period from 1982 to 2022. The data shows that the number of summer days (days with daily maximum temperature- TX, exceeding 25°C) has been decreasing in all districts of Bihar during the study period. The magnitude of the decrease ranges from -20 to -51.44, indicating that the reduction in summer days varies across different districts. The number of tropical nights (days with daily minimum temperature-TN, exceeding 20°C) has been increasing in all districts of Bihar over the 40-year period. The magnitude of the increase ranges from 7.12 (West Champaran) to 14.08 (Kishanganj), highlighting variations in warming nights across different districts. Based on the trends observed in both SU25 and TR20, it can be concluded that Bihar has experienced cooler days and warmer nights over the last 40 years (1982-2022). Indices TX90p and TX10p support the same trend observed in SU25 and TR20, implying an overall shift towards cooler days and warmer nights in Bihar. 3.2. Percentile-based annual temperature indices Fig. 3(a–d) illustrates the trends in temperature extremes for both days and nights across various districts of Bihar during the 40-year period (1982-2022). Warm days (TX90p) are showing declining trend in all districts of Bihar, with a magnitude ranging from -15.4 to -5.16 during the study period. Unlike TX90p, warm nights (TN90p) appeared to have increased trend in most districts, with a magnitude varying from 5.32 (Nalanda and Vaishali) to 1.32 (Katihar), except Kishanganj, Khagaria, Lakhisarai, where decreasing trend was observed (Fig. b). A dramatic increase in cold days were observed throughout the state, showing a gradient from east towards west. Maximum increase was observed in districts located in the east, and the Tarai region of Himalayas. The trend in case of cold nights (TN10p), however, showed decrease throughout the state, except in few districts. It is important to note here that in few of the districts such as Kishanganj ,Khagaria, and Lakhisarai, cold nights are increasing during winter, with a decrease of warm nights during the respective summer seasons. Indices indicating the daytime temperature extremes (TX90p and TX10p) suggests that summer days have become relatively colder and the winter days have become relatively warmer during the 40-year study period. 3.3. Absolute annual temperature indices Figure 4 illustrates the trends in extreme temperature indices—TXx (maximum of maximum), TNx (maximum of minimum), TXn (minimum of maximum), and TNn (minimum of minimum) temperatures—across various districts of Bihar during the 40-year period (1982-2022). Monthly maximum value of TXx over the period of 40-year period (1982-2022) is showing decreasing trend in 28 districts of Bihar. However, it's noteworthy that there are 10 south-western districts, namely Buxar (-0.2), Kaimur (0), Rohtas (0), Aurangabad (-0.44), Gaya (-0.72) and Arwal (-0.36), Patna (-1.12), Bhojpur (-0.8), Nawada (-1.2), Sheikhpura (-1.2) and Siwan (-1.36), where an opposing trend has been observed. These districts have shown an increasing trend in the monthly TXx over the same 40-year period. This could potentially be a result of localized factors or unique climatic conditions in these regions Conversely, the analysis focuses on the monthly maximum of daily minimum temperatures (TNx) reveals a different pattern. Unlike TXx, TNx shows an increasing trend throughout the state, indicating that the highest daily minimum temperatures in most districts have been rising over the 40-year period. However, there are two exceptions, Katihar and Jamui, where a decreasing trend in TNx has been observed. In conclusion, the 40-year analysis of temperature trends in Bihar shows a majority of districts experiencing a decline in the monthly maximum value of daily maximum temperature (TXx), but ten south-western districts are seeing an increase. Additionally, the monthly TNx is increasing across most districts, with only Katihar and Jamui showing a decreasing trend. These findings highlight the spatial variability of temperature trends within the state, and they call for further research to understand the underlying causes and potential impacts on the environment and communities in these regions. Based on the analysis of the monthly minimum value of daily TXn and daily TNn trends in Bihar's districts over the 40-year period (1982-2022), Based on the findings, the key conclusions are as follows: 1. Monthly TXn-The majority of districts in Bihar show an increasing trend in the monthly minimum value of daily maximum temperature (TXn). This suggests that, on average, the lowest daily maximum temperatures in most districts have been rising over the study period. The only exception is the district of Katihar, where a decreasing trend in the monthly TXn has been observed. This indicates that in Katihar, the lowest daily maximum temperatures have been declining over the 40-year period. 2. Monthly TNn- In contrast to TXn, the analysis of the monthly TNn shows a decreasing trend in 14 districts of Bihar. This implies that, on average, the lowest daily minimum temperatures in these districts have been decreasing over the 40-year period. It's important to note that the remaining districts in Bihar have not shown a decreasing trend in TNn, which suggests that they may either have an increasing trend or exhibit no significant change in the lowest daily minimum temperatures. Overall, these temperature trends highlight the spatial variability of climate patterns within Bihar. While the majority of districts experience an increasing trend in the lowest daily maximum temperatures, Katihar stands out as an exception. Similarly, the decreasing trend in the lowest daily minimum temperatures is observed in 14 districts, while the situation may be different in the remaining districts. These findings are significant as they indicate shifts in temperature patterns across Bihar, Such trends could affect key sectors like agriculture, water management, and public health. Therefore, ongoing research and systematic monitoring are vital to uncover the factors driving these changes and to evaluate their consequences for ecosystems, agriculture, and human well-being in the region. Figures 5 illustrate the patterns observed in the yearly count of days when the maximum temperature exceeds 30 degrees Celsius (Txge30) and 35 degrees Celsius (Txge35) across various districts in Bihar over a span of 40 years, from 1982 to 2022. Txge30 is decreasing in the Eastern and North-Eastern districts of Bihar over the 40-year period. Txge30 is increasing in specific districts, namely Sitamarhi and Supaul in the north-eastern region, and Kaimur, Rohtas, and Bhojpur in the south-western region. Txge35 is showing an increasing trend in the majority of districts in Bihar, with 23 out of 38 districts experiencing increasing over the 40-year period. The district of Araria is an exception, where Txge35 is showing a decreasing trend. 3.4 Extreme rainfall characteristics across Bihar Annual Count of Days with PRCP (Precipitation) equal to or more than 10 mm (R10): In two districts, Buxar and Bhojpur, the annual count of days with precipitation equal to or more than 10 mm (R10) is decreasing over the observed period. This trend indicates that these districts are experiencing fewer days with significant rainfall events. In contrast, the remaining districts show an increasing trend in the annual count of R10 days. This suggests that in those districts, the number of days with relatively heavier rainfall (10 mm or more) is on the rise. Annual Count of Days with PRCP equal to or more than 20 mm decreasing in central and western districts. In three districts, namely Bhojpur, Nawada, and Sheikhpura, there is a decreasing trend in the annual count of days with precipitation equal to or more than 20mm. This indicates a decline in the number of days with substantial rainfall (20mm or more) in these districts. Eastern districts experiencing increasing trend while other districts of the Bihar as facing the decreasing trend during the study period. Annual Count of Days with PRCP equal to or more than 30 mm: Nine central and southern districts are showing a most decreasing trend in the annual count of days with precipitation equal to or more than 30mm. This trend suggests that these districts are experiencing a reduction in the number of days with heavy rainfall events (30mm or more). Increasing trend was observed in most of the eastern districts, West Champaran and Gopalganj. While other districts are also facing decreasing trend. In summary, the data indicates that Buxar and Bhojpur districts are experiencing a decrease in the number of days with significant rainfall events (R10). Additionally, Bhojpur, Nawada, and Sheikhpura districts are witnessing a decline in the annual count of days with heavier rainfall (R20). Moreover, nine districts are showing a decreasing trend in the number of days with heavy precipitation (R30). These findings are essential for understanding the changing precipitation patterns in the mentioned regions and may significantly impact agriculture, water availability, and disaster response efforts in the region. Further analysis and research are necessary to ascertain the reasons behind these trends and their potential impacts on the local environment and communities. 1. Monthly Maximum 1-Day Precipitation (Rx1day): - There is an increasing trend in the monthly maximum 1-day precipitation (Rx1day) in the eastern districts of Bihar. This indicates that these districts are experiencing more instances of heavy rainfall within a single day over the 40-year period. This trend could have implications for flash floods, erosion, and water management in the region. 2. Monthly Maximum Consecutive 3-Day Precipitation (Rx3day): - Rx3day is increasing in most of the eastern districts of Bihar over the 40-year period. This suggests that these districts are observing more occurrences of heavy rainfall that last for three consecutive days. This trend of extended heavy rainfall events can contribute to flooding, saturation of soil, and potential damage to crops and infrastructure. 3. Monthly Maximum Consecutive 5-Day Precipitation (Rx5day): - In 11 central and western districts of Bihar, Rx5day is showing an increasing trend over the 40-year period. Among these districts, Kishanganj, Katihar, and Western Champaran stand out with the maximum increase in Rx5day. This indicates that these districts are experiencing more instances of heavy rainfall lasting for five consecutive days, potentially leading to prolonged flooding and water-related issues. The trend suggests a significant trend of increasing heavy rainfall events in different time frames (1-day, 3-day, and 5-day) in Bihar. The eastern districts are witnessing an increase in both Rx1day and Rx3day, which indicates an elevated risk of short-term and medium-term heavy precipitation events, respectively. Meanwhile, in the central and western districts, there is an increasing trend in Rx5day, highlighting the rise in prolonged heavy rainfall events over five consecutive days. These changing precipitation patterns can have wide-ranging impacts on agriculture, water resources, infrastructure, and overall regional development. It is crucial for policymakers, urban planners, and agricultural authorities to consider these trends when formulating adaptation and mitigation strategies to cope with the potential consequences of increased heavy rainfall in Bihar. Additionally, further research and local-level studies can help understand the underlying drivers of these trends and their potential implications for various sectors and communities in the affected regions. Figure 7 illustrates the trends in consecutive dry days (CDD), consecutive wet days (CWD), and simple daily precipitation intensity index (SDII) across various districts of Bihar over the 40-year period from 1982 to 2022. Based on the spatial and temporal patterns observed, the following conclusions can be drawn: 1.CDD: In most of the districts, there is an increasing trend in the number of consecutive dry days, indicating longer periods without rainfall. However, in three districts (Supaul, Saharsa, and Purnea), a decreasing trend in consecutive dry days was observed, suggesting that these districts have experienced more frequent rainfall events. 2. CWD- The majority of districts in western and southern parts of Bihar showed a decreasing trend in consecutive wet days over the study period. The district of Kishanganj stands out with the maximum increase in consecutive wet days, indicating a rise in continuous wet periods. 3. SDII- There is significant heterogeneity in the trends of average daily precipitation intensity across all districts in Bihar. The eastern districts adjacent to the Himalayas experienced the maximum increase in precipitation intensity, indicating a possible influence of the mountainous region on precipitation patterns. Conversely, most of the southern, central, and northern districts displayed a decreasing trend in average daily precipitation intensity, suggesting a reduction in the intensity of daily rainfall in these areas. Overall, these trends suggest changes in the rainfall patterns across different districts in Bihar over the 40-year period. The increasing number of consecutive dry days in most districts and the decreasing consecutive wet days in certain regions may have implications for water availability and agricultural practices. The observed heterogeneity in average daily precipitation intensity further highlights the complexity of climate patterns in Bihar, with certain regions experiencing more pronounced changes than others. Policymakers and local authorities should take these trends into account while formulating climate adaptation and disaster management strategies to mitigate potential impacts on various sectors in the state. Figures 8 present the trends observed in the total annual precipitation from R95p and R99P across different districts in Bihar during a 40-year period from 1982 to 2022. 1. (R95p) - Total annual precipitation from very wet days- There is a decreasing trend in (R95p) in most of the central and southern districts of Bihar, including two western districts, Kaimur, and Rohtas. This suggests that these regions are experiencing a decline in the total annual precipitation from very wet days. The areas adjacent to the Himalayan districts, on the other hand, show the maximum increase in (R95p). This indicates an upward trend in the total annual precipitation from very wet days in these regions. 2. (R99P) - Total annual precipitation from extremely wet days - The eastern districts of Bihar show the maximum increase in (R99P), indicating a rising trend in the total annual precipitation from extremely wet days in those areas. Most of the central districts, however, exhibit a decreasing trend in (R99P), suggesting a decline in the total annual precipitation from extremely wet days in these regions. These trends in total annual precipitation from R95p and R99P have implications for water resources, agriculture, and flood management in the respective regions. Further research and analysis are essential to delve into the underlying dynamics of the factors driving these trends and the extent to which they could affect the the environment and communities in Bihar. Figures 9 depict the variations in annual precipitation (PRCPTOT), the proportion of very wet days (R95PTOT), and the proportion of extremely wet days (R99PTOT) concerning total precipitation in different districts of Bihar over a 40-year period from 1982 to 2022. During the study period, there is heterogeneity in annual precipitation (PRCPTOT) across the districts of Bihar. The eastern districts and one extreme northern district, West Champaran, have experienced an increasing trend in annual precipitation over the 40-year span. In contrast, five western districts, namely Siwan, Saran, Patna, and Bhojpur, have shown a decreasing trend in annual precipitation. The contribution of R95PTOT to total precipitation exhibits an increasing trend in districts situated near the Himalayas and in the districts of West Champaran and Gopalganj. Conversely, the contribution of R99PTOT to total precipitation has seen an increasing trend specifically in the district of Kishanganj. However, a concerning observation is that the remaining districts, including Kaimur, Rohtas, Siwan, Saran, Samastipur, Vaishali, Darbhanga, and Muzaffarpur, have shown a severe decreasing trend in the proportion of extremely wet days, which could have potential implications for water availability and the risk of extreme precipitation events. Overall, these trends highlight the spatial variability in precipitation patterns in Bihar over the 40-year period, with some districts experiencing increased annual precipitation and wet days, while others face decreasing trends in both very wet and extremely wet days. Policymakers and local authorities should closely monitor these changes and consider their potential impacts on water resources, agriculture, and disaster preparedness in the respective districts. Figures 10 provide an overview of the variations in the R95p and R99P in Bihar over a 40-year period from 1982 to 2022. The trends observed are as follows: 1. R95p- There is an increasing trend in the number of very wet days in the eastern districts of Bihar, as well as in the districts of West Champaran and Gopalganj. In contrast, most of the central and southern districts of Bihar are experiencing a decreasing trend in the number of very wet days during the 40-year study period. 2. R99P - Similar to the trends in very wet days, there is an increasing trend in the number of extremely wet days in the eastern districts of Bihar over the 40-year period (1982-2022). However, six central districts, namely Siwan, Saran, Vaishali, Samastipur, Muzaffarpur, Bhojpur, Nalanda, and Darbhanga, are showing a decreasing trend in the number of extremely wet days. The findings indicate that the eastern districts, along with West Champaran and Gopalganj, are experiencing an upward trend in both very wet and extremely wet days, suggesting an increase in heavy rainfall events in these regions. Conversely, most of the central and southern districts are witnessing a decline in such extreme precipitation events, which might have implications for water resource management and potential flood risks. These trends highlight the spatial variability of wet and extremely wet days in Bihar over the 40-year period and underscore the importance of regional considerations when studying climate patterns and planning for climate change impacts. Policymakers and stakeholders should take these trends into account while developing strategies for disaster preparedness, water management, and agricultural practices in the different districts of Bihar. 4. CONCLUSION In conclusion, a comprehensive analysis of Bihar's climate trends over the 40-year study period reveals significant shifts in various meteorological indicators. The reduction in the number of summer days across all districts signifies a notable cooling trend. Concurrently, the escalating occurrence of tropical nights underscores the intensifying warmth during nights. While TX90p uniformly decline, TN90p surge in most districts, barring a few exceptions. TXx demonstrate diverse trends, with some south-western districts witnessing temperature upswings, in contrast to the prevailing decrease. Moreover, the mounting trend in TNx suggests rising minimum temperatures across the state, except in Katihar and Jamui. The intricate interplay between heavy rainfall patterns emerges as a critical factor. Bihar's shifting climate reveals escalating heavy rainfall events, varying across temporal scales and regions. Eastern districts confront increased short and medium-term heavy precipitation risks, while the central and western regions experience prolonged heavy rainfall episodes over five days (Rx5day). Furthermore, the changing frequency of dry and wet days carries implications for water resources and agriculture. As consecutive dry days rise and consecutive wet days decline in specific areas, challenges to water availability and agricultural practices become apparent. The varying intensity of average daily precipitation underscores Bihar's climatic intricacies, with disparities across regions. Collectively, these trends signify a complex climate scenario in Bihar, underlining the need for region-specific adaptation strategies and heightened awareness about the changing environmental dynamics. Declarations Acknowledgements- The authors gratefully acknowledge the NASA Prediction of Worldwide Energy Resources (POWER) project for providing free access to climate data through its online portal (https://power.larc.nasa.gov). We also thank the Expert Team on Climate Change Detection and Indices (ETCCDI) and the Australian Bureau of Meteorology for the development and availability of the Climpact v2 software used in this study. Conflict of Interest: The authors declare that they have no conflict of interest. Funding: No funding agency was involved. Data Availability Statement- The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. All authors confirm that we have read, understood and followed to the “Ethical Responsibilities of Authors” outlined in the Instructions for Authors. We are also aware that, except for minor exceptions, authorship changes cannot be made once the manuscript has been submitted. Consent for publication - Not applicable. Competing interests - The authors declare no competing interests. Clinical Trial Number -Not applicable References IPCC (Intergovernmental Panel on Climate Change). (2023). Summary for policymakers. In Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (H.Lee&J.Romero, Eds.). IPCC. https://doi.org/10.59327/IPCC/AR6-9789291691647.001 Abbass K, Qasim MZ, Song H, Murshed M, Mahmood H, Younis I. 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14:45:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":141765,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGeographical variation in the trend patterns of (a) summer days (SU25) and (b) tropical nights (TR20).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7731739/v1/a76e55072b6c6ca91b892e1d.png"},{"id":94456893,"identity":"1c6e45cb-20e2-42d9-a9bf-c45ed8a9a062","added_by":"auto","created_at":"2025-10-27 14:45:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":324317,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistrict-wise trends in annual (a) TX90p (warm days), (b) TN90p (warm nights), (c) TX10p (cold days), and (d) TN10p (cold nights) for the period 1982–2022 in Bihar.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7731739/v1/f15bbd1cc099c0b4f99e6d2d.png"},{"id":94457293,"identity":"ae7c24c2-1943-49ac-914c-3672fb8f3d62","added_by":"auto","created_at":"2025-10-27 14:45:40","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":261587,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnnual trends in extreme temperature indices: (a) TXx, (b) TNx, (c) TXn, and (d) TNn, representing annual extremes of daily maximum and minimum temperature\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7731739/v1/42b3435a6a93961e595d2249.png"},{"id":94456759,"identity":"4cb117bb-3e22-4433-ba38-b04dd3fb681b","added_by":"auto","created_at":"2025-10-27 14:45:06","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":147144,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpatial variation in the trends of (a) annual number of days with maximum temperature at least 30\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003eo\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003eC (Txge30) and (b) annual number of days with maximum temperature at least 35\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003eo\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003eC (Txge35)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7731739/v1/f497b2043075c71ac93cfc62.png"},{"id":94457286,"identity":"1a068c2e-ebf4-4b7f-9390-f25c3ffc6a14","added_by":"auto","created_at":"2025-10-27 14:45:39","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":161481,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistrict-wise trend analysis of wet days with rainfall of (a) ≥10 mm, (b) ≥20 mm, and (c) ≥30 mm, and the annual maximum precipitation over (d) a single day, (e) three consecutive days, and (f) five consecutive days.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7731739/v1/9055015faf074c6441e7b22e.png"},{"id":94456962,"identity":"2f4c1467-5640-44d9-85f3-8ab8ffd2205a","added_by":"auto","created_at":"2025-10-27 14:45:18","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":122787,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTemporal patterns in (a) CDD (consecutive dry days), (b) CWD (consecutive wet days), and (c) SDII (average daily precipitation intensity)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-7731739/v1/83a9f49560726236a6ff6b9c.png"},{"id":94456761,"identity":"2f65deef-c82f-44e0-8a8b-c33881a28381","added_by":"auto","created_at":"2025-10-27 14:45:07","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":190062,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnnual precipitation trends associated with (a) very wet days (R95p) and (b) extremely wet days (R99p).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-7731739/v1/c63706d3522e15843b618332.png"},{"id":94457393,"identity":"813613bc-edc4-4f55-a154-ecd027187c2f","added_by":"auto","created_at":"2025-10-27 14:45:50","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":159521,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistrict-wise variation in the trends of (a) total annual precipitation, (b) rainfall contribution from very wet days, and (c) from extremely wet days.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-7731739/v1/a4e28fd8f321df277c7f7322.png"},{"id":102907264,"identity":"cd2713c8-3dcb-47cd-801a-d646bdac9030","added_by":"auto","created_at":"2026-02-18 09:27:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2591524,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7731739/v1/7ac99d45-e228-46da-b6be-4b093c136b95.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Understanding Spatio-Temporal Dynamics of Extreme Rainfall and Temperature Events over state of Bihar, India","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eAddressing climate change is now a major priority on the global agenda, leading to alterations in the Earth's climatic conditions and posing severe threats to ecosystems, human livelihoods, and socio-economic development (IPCC, 2023; Abbass et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e ; IPCC \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Gupta, A. and Pathak, H. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In recent years, the consequences of climate change have become more apparent, showing through severe climatic disturbances, rising temperatures, and erratic precipitation trends (Fallah \u003cem\u003eet al\u003c/em\u003e, 2024; Wegler \u003cem\u003eet al\u003c/em\u003e, 2024; Chisale, 2021; WMO, 2020). Among the various regions affected by these climatic shifts, the Indian subcontinent has experienced substantial variations, leading to pronounced impacts on its agriculture, water resources, and overall environment (Government of India, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Balasubramanian, M., and Birundha, V.D. 2012). The state of Bihar, situated in the eastern part of India, bears witness to the multifaceted implications of climate change on its environment and society (Gupta et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Government of Bihar, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Tesfaye et al \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Roy et al \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Singh et al \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The region's predominantly agricultural economy and large population are predominantly vulnerable to extreme weather events, making it essential to comprehend the spatio-temporal dynamics of precipitation and temperature extremes in this area (Jain, et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Warwade, et al, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The present investigation is intended to conduct an in-depth examination of these extremes to gain deeper insights into their patterns, trends, and potential implications for Bihar. Numerous studies have focused on investigating the variations of these indices, either related to temperature, precipitation, or both, within the region (Kholodovsky \u003cem\u003eet al\u003c/em\u003e., 2021; Rashiq et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Such investigations yield critical understanding of evolving climate trends, facilitating deeper insight into the inferences of climate change for the state's environment and communities. Furthermore, some researchers have analyzed the Spatial patterns of temperature and precipitation-based extreme indices across different districts and river basins within Bihar (Chhabra and Haris, 2015). These localized studies offer critical information on how climate change affects specific regions within the state, aiding in the formulating targeted adaptation and mitigation approaches (Sen, Z. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Moreover, to assess potential impacts in Bihar, several studies have explored how temperature and rainfall indices may behave across various climate scenarios (Rajput et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e and Zakwan \u003cem\u003eet al\u003c/em\u003e., 2019). These future projections help policymakers and stakeholders in understanding potential climate risks and taking proactive measures to build resilience (Gellens \u003cem\u003eet al\u003c/em\u003e., 2000; Sen, Z., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Jain et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn addition to studying these indices within the Bihar region, some researchers have also conducted broader regional or global assessments of extreme weather events (Gajbhiye et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Mondal et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2015\u003c/span\u003e Singh et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Singh et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Sharma \u003cem\u003eet al\u003c/em\u003e., 2023). These comprehensive studies provide valuable context to understand how Bihar's climate trends fit into the larger climate change narrative. In nutshell, research concerning temperature and rainfall extremes across Bihar has been crucial in understanding the changing climate patterns and their potential impacts on the region's environment, agriculture, and society. These studies offer valuable insights that can inform evidence-based policies and strategies to safeguard Bihar's communities, agriculture and ecosystems from the challenges posed by climate change.\u003c/p\u003e"},{"header":"2. MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Study area\u003c/h2\u003e\n \u003cp\u003eBihar, a state in eastern India, is situated between latitudinal range of 24\u0026deg;20\u0026prime; to 27\u0026deg;31\u0026prime; north and longitude 83\u0026deg;19\u0026prime; to 88\u0026deg;17\u0026prime; east, encompassing a considerable latitudinal (approximately 3.5 degrees) and longitudinal (around 5 degrees) span. The state\u0026apos;s unique geographic location is characterized by diverse surroundings, including the Ganges River to the south, the \u003cem\u003eTarai\u003c/em\u003e region and the Himalayan foothills to the north, and the borders with Nepal in the east and Uttar Pradesh in the west. Such diverse topography and geographical features contribute to Bihar experiencing varying climatic conditions. Due to its geographical positioning, Bihar encounters a mix of tropical, sub-tropical, and temperate climate types, with distinctive variations in weather patterns across its regions. The mean maximum temperature exhibits considerable variability throughout the state, influenced by factors such as altitude, proximity to water bodies, and seasonal changes. The summer months witness higher temperatures, while winters can be relatively cooler, especially in the northern regions. Bihar receives majority of precipitation during monsoon season. The region\u0026rsquo;s rainfall is predominantly influenced by the southwest monsoon, which stays active from June to September and delivers most of the state\u0026rsquo;s annual precipitation. In the state diverse precipitation pattern contributes to a wide range of ecological settings and supports a rich and varied array of flora and fauna across Bihar.\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Data collection\u003c/h2\u003e\n \u003cp\u003eThe study utilized daily point-scale meteorological data for all districts of Bihar, retrieved from the NASA POWER project, which operates under NASA\u0026rsquo;s Earth Science Research Program (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://power.larc.nasa.gov/data-access-viewer/\u003c/span\u003e\u003c/span\u003e). The NASA POWER dataset offers solar and meteorological variables at a global spatial resolution of 0.5\u0026deg; \u0026times; 0.5\u0026deg;, available from 1981 onward. This analysis included daily temperature extremes (maximum and minimum) and rainfall as key parameters, collected for 38 locations across Bihar for the period 1982\u0026ndash;2022. The inclusion of maximum and minimum temperature data along with rainfall measurements provides a comprehensive picture of the climatic conditions in Bihar over this extensive time span. This dataset is invaluable for conducting detailed analyses and drawing insightful conclusions regarding the long-term weather patterns and trends within the region. The utilization of NASA POWER\u0026apos;s spatially-resolved and temporally-extended dataset enhances the accuracy and reliability of the research findings, contributing to a deeper understanding of Bihar\u0026apos;s climatic dynamics.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Extreme climate indices\u003c/h2\u003e\n \u003cp\u003eTo assess climate variability, this study utilizes 24 standardized indices recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI) and the Expert Team on Sector-Specific Climate Indices (ET-SCI) to examine trends in temperature and precipitation extremes, as detailed in Table-1. Climpact v2 software was employed to derive the climate indices using daily precipitation and temperature (maximum and minimum) data. Prior to computing these extreme indices, a thorough data quality control process was conducted on the dataset. Comprehensive instructions for utilizing Climpact v2, whether with a single station dataset or multiple stations, can be accessed at climpact/www/user_guide/Climpact_user_guide.md at master\u0026middot; ARCCSS-extremes/climpact \u0026middot; GitHub.\u003c/p\u003e\n \u003cp\u003eTable-1 outlines the classification of climate extremes into four types: intensity-based, duration-based, absolute threshold-based, and relative threshold-based indices. This investigation seeks to shed light on the trends related to these indices, offering insights into how temperature and precipitation extremes have evolved over time. The Climpact v2 software\u0026apos;s capabilities enhance the precision and credibility of the analysis, contributing to a more nuanced understanding of the occurrence and characteristics of extreme climatic conditions.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable-1\u003c/strong\u003e\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"101%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eS.N.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eName\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDescription\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eET\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnit\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eCDD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eConsecutive dry days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eMaximum number of consecutive days with RR \u0026lt; 1mm (RR-Rainfall)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eETCCDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003edays\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eCWD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eConsecutive wet days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eMaximum length of wet spell, maximum number of consecutive days with RR \u0026ge; 1mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eETCCDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003edays\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003ePRCPTOT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eTotal precipitation in wet days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eAnnual total precipitation in wet days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eETCCDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003emm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eR10mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eHeavy precipitation days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eAnnual count of days when PRCP\u0026ge; 10mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eETCCDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003edays\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eR20mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eVery heavy precipitation days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eAnnual count of days when PRCP\u0026ge; 20mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eETCCDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003edays\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eR30mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eExtreme precipitation days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eAnnual count of days when PRCP\u0026ge; 30mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eETCCDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003edays\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eR95pTOT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eContribution from very wet days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eAnnual total PRCP when RR \u0026gt; 95p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eET-SCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eR99pTOT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eContribution from extremely wet days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eAnnual total PRCP when RR \u0026gt; 99p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eET-SCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eRx1day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eMax 1-day precipitation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eMonthly maximum 1-day precipitation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eETCCDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003emm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eRx3day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eMax 3-day precipitation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eMonthly maximum consecutive 3-day precipitation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eETCCDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003emm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e11\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eRx5day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eMax 5-day precipitation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eMonthly maximum consecutive 5-day precipitation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eETCCDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003emm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e12\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eSDII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eSimple daily intensity index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eSimple precipitation intensity index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eETCCDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003emm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e13\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eSU25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eSummer days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eAnnual count of days when TX (daily maximum temperature) \u0026gt; 25\u003csup\u003eo\u003c/sup\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eETCCDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003edays\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eTN10p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eCold nights\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003ePercentage of days when TN \u0026lt; 10\u003csup\u003eth\u003c/sup\u003e percentile\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eETCCDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e% days\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e15\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eTN90p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eWarm nights\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003ePercentage of days when TN \u0026gt; 90\u003csup\u003eth\u003c/sup\u003e percentile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eETCCDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e% days\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e16\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eTNn\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eMin TN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eMonthly minimum value of daily minimum temperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eETCCDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e17\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eTNx\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eMax TN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eMonthly maximum value of daily minimum temperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eETCCDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e18\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eTR20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eTropical nights\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eAnnual count of days when TN (daily minimum temperature) \u0026gt; 20\u003csup\u003eo\u003c/sup\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eETCCDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003edays\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e19\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eTX10p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eCold days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003ePercentage of days when TX \u0026lt; 10\u003csup\u003eth\u003c/sup\u003e percentile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eETCCDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e% days\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e20\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eTX90p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eWarm days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003ePercentage of days when TX \u0026gt; 90\u003csup\u003eth\u003c/sup\u003e percentile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eETCCDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e% days\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e21\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eTXge30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eTX of at least 30 \u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eAnnual number of days with TX of at least 30 \u0026deg;C\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eET-SCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003edays\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e22\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eTXge35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eTX of at least 35 \u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eAnnual number of days with TX of at least 35 \u0026deg;C\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eET-SCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003edays\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e23\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eTXn\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eMin TX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eMonthly minimum value of daily maximum temperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eETCCDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e24\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eTXx\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eMax TX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eMonthly maximum value of daily maximum temperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eETCCDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eReference: https://climpact-sci.org/indices\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"3. RESULTS AND DISCUSSION","content":"\u003cp\u003e\u003cstrong\u003e3.1 Temperature thresholds\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFig. (a) and (b) represents trend of SU25 and TR20 in different districts of Bihar over a 40-year period from 1982 to 2022. \u0026nbsp;The data shows that the number of summer days (days with daily maximum temperature- TX, exceeding 25\u0026deg;C) has been decreasing in all districts of Bihar during the study period. The magnitude of the decrease ranges from -20 to -51.44, indicating that the reduction in summer days varies across different districts. The number of tropical nights (days with daily minimum temperature-TN, exceeding 20\u0026deg;C) has been increasing in all districts of Bihar over the 40-year period. The magnitude of the increase ranges from 7.12 (West Champaran) to 14.08 (Kishanganj), highlighting variations in warming nights across different districts. Based on the trends observed in both SU25 and TR20, it can be concluded that Bihar has experienced cooler days and warmer nights over the last 40 years (1982-2022). Indices TX90p and TX10p support the same trend observed in SU25 and TR20, implying an overall shift towards cooler days and warmer nights in Bihar.\u003c/p\u003e\n\u003ch3\u003e3.2.\u0026nbsp;Percentile-based annual temperature indices\u003c/h3\u003e\n\u003cp\u003eFig. 3(a\u0026ndash;d) illustrates the trends in temperature extremes for both days and nights across various districts of Bihar during the 40-year period (1982-2022). Warm days (TX90p) are showing declining trend in all districts of Bihar, with a magnitude ranging from -15.4 to -5.16 during the study period. Unlike TX90p, warm nights (TN90p) appeared to have increased trend in most districts, with a magnitude varying from 5.32 (Nalanda and Vaishali) to 1.32 (Katihar), except Kishanganj, Khagaria, Lakhisarai, where decreasing trend was observed (Fig. b). \u0026nbsp;A dramatic increase in cold days were observed throughout the state, showing a gradient from east towards west. Maximum increase was observed in districts located in the east, and the \u003cem\u003eTarai\u0026nbsp;\u003c/em\u003eregion of Himalayas. The trend in case of cold nights (TN10p), however, showed decrease throughout the state, except in few districts. It is important to note here that in few of the districts such as Kishanganj ,Khagaria, and Lakhisarai, cold nights are increasing during winter, with a decrease of warm nights during the respective summer seasons. Indices indicating the daytime temperature extremes (TX90p and TX10p) suggests that summer days have become relatively colder and the winter days have become relatively warmer during the 40-year study period. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3. Absolute annual temperature indices\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 4 illustrates the trends in extreme temperature indices\u0026mdash;TXx (maximum of maximum), TNx (maximum of minimum), TXn (minimum of maximum), and TNn (minimum of minimum) temperatures\u0026mdash;across various districts of Bihar during the 40-year period (1982-2022). Monthly maximum value of TXx \u0026nbsp;over the period of 40-year period (1982-2022) is showing decreasing trend in 28 districts of Bihar. \u0026nbsp;However, it\u0026apos;s noteworthy that there are 10 south-western districts, namely Buxar (-0.2), Kaimur (0), Rohtas (0), Aurangabad (-0.44), Gaya \u0026nbsp;(-0.72) and Arwal \u0026nbsp;(-0.36), Patna (-1.12), Bhojpur (-0.8), Nawada (-1.2), Sheikhpura (-1.2) and \u0026nbsp; Siwan (-1.36), where an opposing trend has been observed. These districts have shown an increasing trend in the monthly TXx over the same 40-year period. This could potentially be a result of localized factors or unique climatic conditions in these regions Conversely, the analysis focuses on the monthly maximum of daily minimum temperatures (TNx) reveals a different pattern. Unlike TXx, TNx shows an increasing trend throughout the state, indicating that the highest daily minimum temperatures in most districts have been rising over the 40-year period. However, there are two exceptions, Katihar and Jamui, where a decreasing trend in TNx has been observed. In conclusion, the 40-year analysis of temperature trends in Bihar shows a majority of districts experiencing a decline in the monthly maximum value of daily maximum temperature (TXx), but ten south-western districts are seeing an increase. Additionally, the monthly TNx is increasing across most districts, with only Katihar and Jamui showing a decreasing trend. These findings highlight the spatial variability of temperature trends within the state, and they call for further research to understand the underlying causes and potential impacts on the environment and communities in these regions. Based on the analysis of the monthly minimum value of daily TXn and daily TNn trends in Bihar\u0026apos;s districts over the 40-year period (1982-2022), Based on the findings, the key conclusions are as follows:\u003c/p\u003e\n\u003cp\u003e1. Monthly TXn-The majority of districts in Bihar show an increasing trend in the monthly minimum value of daily maximum temperature (TXn). This suggests that, on average, the lowest daily maximum temperatures in most districts have been rising over the study period. The only exception is the district of Katihar, where a decreasing trend in the monthly TXn has been observed. This indicates that in Katihar, the lowest daily maximum temperatures have been declining over the 40-year period.\u003c/p\u003e\n\u003cp\u003e2. Monthly TNn- In contrast to TXn, the analysis of the monthly TNn shows a decreasing trend in 14 districts of Bihar. This implies that, on average, the lowest daily minimum temperatures in these districts have been decreasing over the 40-year period. It\u0026apos;s important to note that the remaining districts in Bihar have not shown a decreasing trend in TNn, which suggests that they may either have an increasing trend or exhibit no significant change in the lowest daily minimum temperatures.\u003c/p\u003e\n\u003cp\u003eOverall, these temperature trends highlight the spatial variability of climate patterns within Bihar. While the majority of districts experience an increasing trend in the lowest daily maximum temperatures, Katihar stands out as an exception. Similarly, the decreasing trend in the lowest daily minimum temperatures is observed in 14 districts, while the situation may be different in the remaining districts. These findings are significant as they indicate shifts in temperature patterns across Bihar, Such trends could affect key sectors like agriculture, water management, and public health. Therefore, ongoing research and systematic monitoring are vital to uncover the factors driving these changes and to evaluate their consequences for ecosystems, agriculture, and human well-being in the region.\u003c/p\u003e\n\u003cp\u003eFigures 5 illustrate the patterns observed in the yearly count of days when the maximum temperature exceeds 30 degrees Celsius (Txge30) and 35 degrees Celsius (Txge35) across various districts in Bihar over a span of 40 years, from 1982 to 2022. Txge30 is decreasing in the Eastern and North-Eastern districts of Bihar over the 40-year period. Txge30 is increasing in specific districts, namely Sitamarhi and Supaul in the north-eastern region, and Kaimur, Rohtas, and Bhojpur in the south-western region. Txge35 is showing an increasing trend in the majority of districts in Bihar, with 23 out of 38 districts experiencing increasing over the 40-year period. The district of Araria is an exception, where Txge35 is showing a decreasing trend.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Extreme rainfall characteristics across Bihar\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnnual Count of Days with PRCP (Precipitation) equal to or more than 10 mm (R10): In two districts, Buxar and Bhojpur, the annual count of days with precipitation equal to or more than 10 mm (R10) is decreasing over the observed period. This trend indicates that these districts are experiencing fewer days with significant rainfall events. In contrast, the remaining districts show an increasing trend in the annual count of R10 days. This suggests that in those districts, the number of days with relatively heavier rainfall (10 mm or more) is on the rise.\u003c/p\u003e\n\u003cp\u003eAnnual Count of Days with PRCP equal to or more than 20 mm decreasing in central and western districts. In three districts, namely Bhojpur, Nawada, and Sheikhpura, there is a decreasing trend in the annual count of days with precipitation equal to or more than 20mm. This indicates a decline in the number of days with substantial rainfall (20mm or more) in these districts. Eastern districts experiencing increasing trend while other districts of the Bihar as facing the decreasing trend during the study period.\u003c/p\u003e\n\u003cp\u003eAnnual Count of Days with PRCP equal to or more than 30 mm: Nine central and southern districts are showing a most decreasing trend in the annual count of days with precipitation equal to or more than 30mm. This trend suggests that these districts are experiencing a reduction in the number of days with heavy rainfall events (30mm or more). Increasing trend was observed in most of the eastern districts, West Champaran and Gopalganj. While other districts are also facing decreasing trend.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn summary, the data indicates that Buxar and Bhojpur districts are experiencing a decrease in the number of days with significant rainfall events (R10). Additionally, Bhojpur, Nawada, and Sheikhpura districts are witnessing a decline in the annual count of days with heavier rainfall (R20). Moreover, nine districts are showing a decreasing trend in the number of days with heavy precipitation (R30). These findings are essential for understanding the changing precipitation patterns in the mentioned regions and may significantly impact agriculture, water availability, and disaster response efforts in the region. Further analysis and research are necessary to ascertain the reasons behind these trends and their potential impacts on the local environment and communities.\u003c/p\u003e\n\u003cp\u003e1. Monthly Maximum 1-Day Precipitation (Rx1day): - There is an increasing trend in the monthly maximum 1-day precipitation (Rx1day) in the eastern districts of Bihar. This indicates that these districts are experiencing more instances of heavy rainfall within a single day over the 40-year period. This trend could have implications for flash floods, erosion, and water management in the region.\u003c/p\u003e\n\u003cp\u003e2. Monthly Maximum Consecutive 3-Day Precipitation (Rx3day):\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp;- Rx3day is increasing in most of the eastern districts of Bihar over the 40-year period. This suggests that these districts are observing more occurrences of heavy rainfall that last for three consecutive days. This trend of extended heavy rainfall events can contribute to flooding, saturation of soil, and potential damage to crops and infrastructure.\u003c/p\u003e\n\u003cp\u003e3. Monthly Maximum Consecutive 5-Day Precipitation (Rx5day):\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp;- In 11 central and western districts of Bihar, Rx5day is showing an increasing trend over the 40-year period. Among these districts, Kishanganj, Katihar, and Western Champaran stand out with the maximum increase in Rx5day. This indicates that these districts are experiencing more instances of heavy rainfall lasting for five consecutive days, potentially leading to prolonged flooding and water-related issues.\u003c/p\u003e\n\u003cp\u003eThe trend suggests a significant trend of increasing heavy rainfall events in different time frames (1-day, 3-day, and 5-day) in Bihar. The eastern districts are witnessing an increase in both Rx1day and Rx3day, which indicates an elevated risk of short-term and medium-term heavy precipitation events, respectively. Meanwhile, in the central and western districts, there is an increasing trend in Rx5day, highlighting the rise in prolonged heavy rainfall events over five consecutive days.\u003c/p\u003e\n\u003cp\u003eThese changing precipitation patterns can have wide-ranging impacts on agriculture, water resources, infrastructure, and overall regional development. It is crucial for policymakers, urban planners, and agricultural authorities to consider these trends when formulating adaptation and mitigation strategies to cope with the potential consequences of increased heavy rainfall in Bihar. Additionally, further research and local-level studies can help understand the underlying drivers of these trends and their potential implications for various sectors and communities in the affected regions.\u003c/p\u003e\n\u003cp\u003eFigure 7 illustrates the trends in consecutive dry days (CDD), consecutive wet days (CWD), and simple daily precipitation intensity index (SDII) across various districts of Bihar over the 40-year period from 1982 to 2022. Based on the spatial and temporal patterns observed, the following conclusions can be drawn:\u003c/p\u003e\n\u003cp\u003e1.CDD: In most of the districts, there is an increasing trend in the number of consecutive dry days, indicating longer periods without rainfall. However, in three districts (Supaul, Saharsa, and Purnea), a decreasing trend in consecutive dry days was observed, suggesting that these districts have experienced more frequent rainfall events.\u003c/p\u003e\n\u003cp\u003e2. CWD- The majority of districts in western and southern parts of Bihar showed a decreasing trend in consecutive wet days over the study period. The district of Kishanganj stands out with the maximum increase in consecutive wet days, indicating a rise in continuous wet periods.\u003c/p\u003e\n\u003cp\u003e3. SDII- There is significant heterogeneity in the trends of average daily precipitation intensity across all districts in Bihar. The eastern districts adjacent to the Himalayas experienced the maximum increase in precipitation intensity, indicating a possible influence of the mountainous region on precipitation patterns. Conversely, most of the southern, central, and northern districts displayed a decreasing trend in average daily precipitation intensity, suggesting a reduction in the intensity of daily rainfall in these areas.\u003c/p\u003e\n\u003cp\u003eOverall, these trends suggest changes in the rainfall patterns across different districts in Bihar over the 40-year period. The increasing number of consecutive dry days in most districts and the decreasing consecutive wet days in certain regions may have implications for water availability and agricultural practices. The observed heterogeneity in average daily precipitation intensity further highlights the complexity of climate patterns in Bihar, with certain regions experiencing more pronounced changes than others. Policymakers and local authorities should take these trends into account while formulating climate adaptation and disaster management strategies to mitigate potential impacts on various sectors in the state.\u003c/p\u003e\n\u003cp\u003eFigures 8 present the trends observed in the total annual precipitation from R95p and R99P across different districts in Bihar during a 40-year period from 1982 to 2022.\u003c/p\u003e\n\u003cp\u003e1. (R95p) - Total annual precipitation from very wet days- There is a decreasing trend in (R95p) in most of the central and southern districts of Bihar, including two western districts, Kaimur, and Rohtas. This suggests that these regions are experiencing a decline in the total annual precipitation from very wet days. The areas adjacent to the Himalayan districts, on the other hand, show the maximum increase in (R95p). This indicates an upward trend in the total annual precipitation from very wet days in these regions.\u003c/p\u003e\n\u003cp\u003e2. (R99P) - Total annual precipitation from extremely wet days - The eastern districts of Bihar show the maximum increase in (R99P), indicating a rising trend in the total annual precipitation from extremely wet days in those areas. Most of the central districts, however, exhibit a decreasing trend in (R99P), suggesting a decline in the total annual precipitation from extremely wet days in these regions.\u003c/p\u003e\n\u003cp\u003eThese trends in total annual precipitation from R95p and R99P have implications for water resources, agriculture, and flood management in the respective regions. Further research and analysis are essential to delve into the underlying dynamics of the factors driving these trends and the extent to which they could affect the the environment and communities in Bihar.\u003c/p\u003e\n\u003cp\u003eFigures 9 depict the variations in annual precipitation (PRCPTOT), the proportion of very wet days (R95PTOT), and the proportion of extremely wet days (R99PTOT) concerning total precipitation in different districts of Bihar over a 40-year period from 1982 to 2022. During the study period, there is heterogeneity in annual precipitation (PRCPTOT) across the districts of Bihar. The eastern districts and one extreme northern district, West Champaran, have experienced an increasing trend in annual precipitation over the 40-year span. In contrast, five western districts, namely Siwan, Saran, Patna, and Bhojpur, have shown a decreasing trend in annual precipitation. The contribution of R95PTOT to total precipitation exhibits an increasing trend in districts situated near the Himalayas and in the districts of West Champaran and Gopalganj.\u003c/p\u003e\n\u003cp\u003eConversely, the contribution of R99PTOT to total precipitation has seen an increasing trend specifically in the district of Kishanganj. However, a concerning observation is that the remaining districts, including Kaimur, Rohtas, Siwan, Saran, Samastipur, Vaishali, Darbhanga, and Muzaffarpur, have shown a severe decreasing trend in the proportion of extremely wet days, which could have potential implications for water availability and the risk of extreme precipitation events. \u0026nbsp;Overall, these trends highlight the spatial variability in precipitation patterns in Bihar over the 40-year period, with some districts experiencing increased annual precipitation and wet days, while others face decreasing trends in both very wet and extremely wet days. Policymakers and local authorities should closely monitor these changes and consider their potential impacts on water resources, agriculture, and disaster preparedness in the respective districts.\u003c/p\u003e\n\u003cp\u003eFigures 10 provide an overview of the variations in the R95p and R99P in Bihar over a 40-year period from 1982 to 2022. The trends observed are as follows:\u003c/p\u003e\n\u003cp\u003e1. R95p- There is an increasing trend in the number of very wet days in the eastern districts of Bihar, as well as in the districts of West Champaran and Gopalganj. In contrast, most of the central and southern districts of Bihar are experiencing a decreasing trend in the number of very wet days during the 40-year study period.\u003c/p\u003e\n\u003cp\u003e2. R99P - Similar to the trends in very wet days, there is an increasing trend in the number of extremely wet days in the eastern districts of Bihar over the 40-year period (1982-2022). However, six central districts, namely Siwan, Saran, Vaishali, Samastipur, Muzaffarpur, Bhojpur, Nalanda, and Darbhanga, are showing a decreasing trend in the number of extremely wet days. The findings indicate that the eastern districts, along with West Champaran and Gopalganj, are experiencing an upward trend in both very wet and extremely wet days, suggesting an increase in heavy rainfall events in these regions. Conversely, most of the central and southern districts are witnessing a decline in such extreme precipitation events, which might have implications for water resource management and potential flood risks.\u003c/p\u003e\n\u003cp\u003eThese trends highlight the spatial variability of wet and extremely wet days in Bihar over the 40-year period and underscore the importance of regional considerations when studying climate patterns and planning for climate change impacts. Policymakers and stakeholders should take these trends into account while developing strategies for disaster preparedness, water management, and agricultural practices in the different districts of Bihar.\u003c/p\u003e"},{"header":"4. CONCLUSION","content":"\u003cp\u003eIn conclusion, a comprehensive analysis of Bihar's climate trends over the 40-year study period reveals significant shifts in various meteorological indicators. The reduction in the number of summer days across all districts signifies a notable cooling trend. Concurrently, the escalating occurrence of tropical nights underscores the intensifying warmth during nights. While TX90p uniformly decline, TN90p surge in most districts, barring a few exceptions. TXx demonstrate diverse trends, with some south-western districts witnessing temperature upswings, in contrast to the prevailing decrease. Moreover, the mounting trend in TNx suggests rising minimum temperatures across the state, except in Katihar and Jamui. The intricate interplay between heavy rainfall patterns emerges as a critical factor. Bihar's shifting climate reveals escalating heavy rainfall events, varying across temporal scales and regions. Eastern districts confront increased short and medium-term heavy precipitation risks, while the central and western regions experience prolonged heavy rainfall episodes over five days (Rx5day). Furthermore, the changing frequency of dry and wet days carries implications for water resources and agriculture. As consecutive dry days rise and consecutive wet days decline in specific areas, challenges to water availability and agricultural practices become apparent. The varying intensity of average daily precipitation underscores Bihar's climatic intricacies, with disparities across regions. Collectively, these trends signify a complex climate scenario in Bihar, underlining the need for region-specific adaptation strategies and heightened awareness about the changing environmental dynamics.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements-\u003c/strong\u003e The authors gratefully acknowledge the NASA Prediction of Worldwide Energy Resources (POWER) project for providing free access to climate data through its online portal (https://power.larc.nasa.gov). We also thank the Expert Team on Climate Change Detection and Indices (ETCCDI) and the Australian Bureau of Meteorology for the development and availability of the Climpact v2 software used in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest:\u003c/strong\u003e The authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e No funding agency was involved.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement-\u003c/strong\u003e The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\n\u003cp\u003eAll authors confirm that we have read, understood and followed to the “Ethical Responsibilities of Authors” outlined in the Instructions for Authors. We are also aware that, except for minor exceptions, authorship changes cannot be made once the manuscript has been submitted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e- Not applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e- The authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Clinical Trial Number\u003c/strong\u003e-Not applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eIPCC (Intergovernmental Panel on Climate Change). (2023). Summary for policymakers. In Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (H.Lee\u0026amp;J.Romero, Eds.). 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Retrieved from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.climpact-sci.org\u003c/span\u003e\u003cspan address=\"https://www.climpact-sci.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eClimpact Project. (n.d.). \u003cem\u003eIndices recommended by ETCCDI and ET-SCI\u003c/em\u003e. Retrieved from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://climpact-sci.org/indices/\u003c/span\u003e\u003cspan address=\"https://climpact-sci.org/indices/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNASA. (2020). \u003cem\u003eNASA Prediction of Worldwide Energy Resources (POWER) Project\u003c/em\u003e. Retrieved from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://power.larc.nasa.gov\u003c/span\u003e\u003cspan address=\"https://power.larc.nasa.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"ETCCDI, ET-SCI, summer days, tropical night, Bihar","lastPublishedDoi":"10.21203/rs.3.rs-7731739/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7731739/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe present study examines changes in extreme temperature and rainfall patterns across Bihar\u0026rsquo;s districts over a span of four decades (1982\u0026ndash;2022), focusing on their spatial and temporal dynamics. Daily weather data for parameters such as maximum temperature, minimum temperature, and rainfall were sourced from the NASA-POWER database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://power.larc.nasa.gov\u003c/span\u003e\u003cspan address=\"https://power.larc.nasa.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to compute climate extreme indices. The formulation of these indices adhered to the standardized approaches suggested by the ETCCDI and ET-SCI for consistent climate change and sector-specific climate analysis. Notably, this is the foremost comprehensive analysis focusing on the temporal trends of climate extremes at the district level in Bihar. Bihar's climate trends spanning four decades exhibit notable transformations in meteorological variables. Reduced summer days hint at a cooling trend, while lengthen tropical nights reflect nocturnal warming. Although warm days decrease uniformly, warm nights surge in most districts. Diverse patterns emerge in monthly maximum temperatures (TXx), with some south-western districts bucking the trend. Rising minimum temperatures are evident except in Katihar and Jamui. Crucially, heavy rainfall dynamics are pivotal, with escalating events varying by time frame and locale. Eastern districts face increased short and medium-term heavy precipitation risks, while the central and western regions encounter prolonged heavy rainfall episodes over five days (Rx5day). Changing dry and wet day frequencies impact water resources and agriculture, accentuating the need for tailored adaptation strategies. Bihar's climatic complexity underscores regional disparities, emphasizing the urgency of responsive environmental awareness and strategies.\u003c/p\u003e","manuscriptTitle":"Understanding Spatio-Temporal Dynamics of Extreme Rainfall and Temperature Events over state of Bihar, India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-27 11:42:37","doi":"10.21203/rs.3.rs-7731739/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5f6991b1-9768-41f1-99c3-7e95443961a3","owner":[],"postedDate":"October 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-18T09:26:26+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-27 11:42:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7731739","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7731739","identity":"rs-7731739","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}