Changing Precipitation Patterns and Extremes in Southern Thailand under Warmer Climate

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Upreti, S.-Y. Simon Wang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4535815/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Aug, 2024 Read the published version in Theoretical and Applied Climatology → Version 1 posted 8 You are reading this latest preprint version Abstract Southern Thailand has experienced significant shifts in precipitation patterns in recent years, exerting substantial impacts on regional water resources and infrastructure systems. This study aims to elucidate these changes and underlying factors based on daily precipitation observations from Nakhon Si Thammarat Province spanning 1980 to 2022. Additionally, data from the Coupled Model Intercomparison Project Phase 6 (CMIP6) is utilized to investigate projected changes in precipitation for 2015-2100 relative to the historical period (1980-2014), employing a comprehensive analysis considering two emissions scenarios (SSP245 and SSP585) across six models. Various precipitation indices are selected to assess trends and statistical significance using the Mann-Kendall test. Both observed and climate model data indicate an increasing precipitation trend in Southern Thailand, with a reduced association with the El Niño-Southern Oscillation (ENSO) under warming conditions. Extreme precipitation indices also exhibit an increasing trend, with total precipitation and the 95th percentile of daily precipitation (R95p) revealing very wet conditions in recent years, projected to continue increasing. Contrastingly, the number of dry days is also mounting, suggesting that both dry and wet extremes will impact Southern Thailand under a warmer climate. The findings from this study provide an early indication of future precipitation and extreme event scenarios, which can inform the development of measures to mitigate climate change-related hazards in the region. CMIP6 Precipitation Extreme ENSO Southern Thailand Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Extreme precipitation refers to unusually intense or prolonged heavy rainfall events that significantly exceed the average precipitation amounts for a given region (Curriero et al., 2001), leading to a stormflow or rapid accumulation of flood water. These events in addition to flooding, often cause landslides and pose threats to infrastructure, agriculture, and ecosystems (Du et al., 2022; Jiang et al., 2014; Nilsson & Svedmark, 2002; Ohl, 2000; Pregnolato et al., 2017; Rosenzweig et al., 2002; Wieczorek & Guzzetti, 1999). Global trends in extreme precipitation show an overall increase in intensity and frequency in recent years (Alexander et al., 2006; Min et al., 2011; Solomon, 2007; Westra et al., 2013). Studies have observed a rising trend in extreme precipitation events across various regions (Karki et al., 2017; Kunkel, 2003; Stocker et al., 2014; Wang et al., 2017). Asian countries, including Thailand, have undergone changes in precipitation patterns, affecting agriculture, water availability, and ecosystems (Hlophe-Ginindza & Mpandeli, 2020). Recent years have seen an increase in extreme precipitation events in Southern Thailand, leading to flash floods, landslides, and disruptions in local (Endo et al., 2009; Limsakul & Singhruck, 2016). The evolving precipitation patterns have raised concerns about the region's climate-related hazard vulnerability, necessitating effective adaptation strategies. Projections for future precipitation trends in Southern Thailand indicate a heightened likelihood of more intense and frequent extreme precipitation events (Arunrat et al., 2022). Climate models suggest that rising temperatures and shifting atmospheric circulation patterns will lead to shifts in monsoonal rainfall timing and intensity, affecting agricultural practices, water availability, and ecological systems (Seth et al., 2019). Additionally, sea-level rise and its interaction with extreme precipitation events pose added challenges for Southern Thailand's coastal areas (Nicholls & Cazenave, 2010). Although climate models offer clues of future precipitation patterns (Kattenberg et al., 1996), these diverse outcomes underscore the complexity and regional variability inherent in climate projections, necessitating a comprehensive approach to understand and address the challenges posed by climate change. The El Nino–Southern Oscillation (ENSO) phenomenon, characterized by variations in sea-surface temperature (SST) and the Southern Oscillation Index (SOI) in the equatorial Pacific Ocean, significantly influences global climate patterns. ENSO events, including El Nino and La Nina, have diverse impacts on precipitation and temperature worldwide (Taschetto et al., 2020). In Asia, particularly in Thailand, ENSO has been linked to various climatic phenomena. For instance, El Niño events often bring warm and dry conditions to Australia, Indonesia, and parts of South America, while La Niña events lead to cooler and wetter conditions in these regions (Kane, 1999; Lau & Wu, 2001; Ropelewski & Halpert, 1987; Trenberth et al., 1998). Understanding the dynamics of ENSO and its teleconnections is crucial for predicting and mitigating the socio-economic impacts of climate variability in Thailand and across Asia. As ENSO events can be predicted with some accuracy, quantifying their relationship with precipitation could enhance precipitation prediction capabilities (Amarasekera et al., 1997). Thus, investigating the linkages between ENSO and precipitation patterns is vital for improving climate forecasting and adaptation strategies in Thailand and other affected regions. This study aims to understand the historical and contemporary extreme precipitation patterns in Southern Thailand, with specific attention to Nakhon Si Thammarat Province (Figure 1a). The investigation incorporates climate models precipitation data to project future changes, considering various scenarios and providing valuable insights into potential climate impacts on the region. Additionally, the study explores the influence of ENSO on extreme precipitation in Southern Thailand, aiming to contribute essential knowledge for effective climate change mitigation and adaptation planning in the region. 2. Material and Methods 2.1 Study Area Nakhon Si Thammarat, a southern province in Thailand, flooding is a recurrent event affecting the entire province (Langkulsen et al., 2022). Every year (Feb 2017, Jan 2017, Dec 2018, Dec 2020, Dec 2021, Dec 2022, Feb 2022, Nov 2023, etc ), it causes lives and damages to infrastructure, agricultural production and severely affects local economic development (Centre for Research on the Epidemiology of Disasters (CRED), 2023). This province has been selected as the focus area of our research to tackle challenges associated with extreme precipitation and flooding and to empower policymakers and stakeholders to make informed decisions to enhance resilience and adaptability to a changing climate. Nakhon Si Thammarat province is characterized by a diverse topography and climate. Bordered by the Gulf of Thailand to the east and the Andaman Sea to the west, this province exhibits a range of elevations. The topography includes low-lying coastal areas along the Gulf of Thailand, which are close to sea level, as well as higher terrain in the central and western regions, reaching elevations of approximately 1800 meters above sea level. The seasonal distribution of rainfall plays a crucial role in shaping the landscape, ecosystems, and overall environmental conditions in Nakhon Si Thammarat, contributing to the region's unique climatic characteristics. The climate in this region is tropical, typical of Thailand, with distinct variations throughout the year. The average temperatures range from 25°C to 32°C. The province experiences a tropical climate, with warm temperatures prevailing throughout the year. The mean annual precipitation is around 2400 mm. The annual rainfall pattern in Nakhon Si Thammarat displays distinct seasonal variations, with the highest precipitation occurring in October, November, December, and January (ONDJ), collectively contributing to over 60% of the total annual rainfall. In contrast, February experiences the lowest rainfall, representing a drier phase in the climate cycle (Figure 1b). Noteworthy changes in the annual rainfall pattern has been observed, particularly from the year 2000 onwards. There is a discernible upward trend in precipitation during January (Figure 1c). This shift in historical precipitation patterns is significant, indicating an increase in January rainfall compared to previous years. 2.2 Datasets Daily observed precipitation data from the Nakhon Si Thammarat Province station (552201), obtained from the Thai Meteorological Department (TMD), was used for this study. The station data covered the period from 1980 to 2022. Additionally, the daily precipitation data from Climate Hazards Group Infrared Precipitation with Station (CHIRPS) was acquired from the Climate Engine app (https://app.climateengine.com), covering the period 1981 to 2022. The CHIRPS data was bias-corrected using the station data and it was used to fill the missing data of the observed station data. In our study focusing on extreme precipitation and its trends as proposed by Expert Team on Climate Change Detection and Indices (ETCCTI) in Nakhon Si Thammarat province, Thailand, we employed the NASA Earth Exchange Global Daily Downscaled Projections archive (NEX-GDDP-CMIP-6). NEX-GDDP-CMIP6 dataset, which serves as a bias-corrected downscaled iteration of Global Climate Models (GCM) (Thrasher et al., 2022). To assess precipitation and its extreme variability in southern Thailand, we obtained the bias-corrected versions of 6 CMIP6 model experiments from source, https://www.nccs.nasa.gov/services/data-collections/land-based-products/nex-gddp-cmip6. These 6 models were also employed by Rojpratak & Supharatid, (2023); Ge et al., (2021); De Silva et al., (2023) and Supharatid et al., (2022) in their previous study focusing on the Southeast Asia (SEA) and Thailand region. Similarly, the NINO3.4 data for same 6 CMIP6 model was obtained from Climate Variability Diagnostics Package (CVDP) (Phillips et al., 2014) from https://www2.cesm.ucar.edu/working_groups/CVC/cvdp/data-repository.html to analyze the relationship between ENSO and annual precipitation. Our investigation spanned historical simulations covering the period from 1980 to 2014 and projected periods extending from 2015 to 2100 under two Shared Socioeconomic Pathways (SSPs): SSP245 and SSP585. The SSP245 (SSP585) is an update of the CMIP5 scenario RCP4.5 (RCP8.5), an additional radiative forcing of 4.5 (8.5) W/m² by the year 2100, with now combined with socioeconomic reasons in CMIP6 future scenario (Thrasher et al., 2022). As, these scenarios form an essential part of climate change study representing the medium (SSP245) and high (SSP585) pathways of future greenhouse gas emissions. Precipitation data was again bias-corrected with the observed precipitation data and further analysis was performed. Table 1 provides a detailed list of the CMIP6 ensemble members employed in our study, thus forming the foundation for our investigation into precipitation patterns, ENSO relationship and extreme variability in the study region. Table 1 List of Bias-corrected CMIP6 models used S.N. Model Name 1 ACCESS-CM2 Australian Community Climate and Earth System Simulator - Climate Model version 2 (Australia) 2 ACCESS-ESM1-5 Australian Community Climate and Earth System Simulator - Earth System Model version 1.5 (Australia) 3 EC EARTH 3CC EC-Earth Climate Model version 3 Coupled Configuration (Europe) 4 INM-CM5-0 Institute of Numerical Mathematics Climate Model version 5.0 (Russia) 5 MIROC-6 Model for Interdisciplinary Research on Climate version 6 (Japan) 6 FGOALS FGOALS Model (China) 2.3 Approach to analysis Approach to analysis of various model outputs and datasets has been adopted from similar exercises undertaken before. For example, the types of extreme precipitation indices proposed by Zhang et al. (2011) as part of the Expert Team on Climate Change Detection and Indices (ETCCTI) offer ways to interpret the patterns. The ETCCTI is a group of climate experts convened by the World Meteorological Organization (WMO) with the goal of developing a suite of climate change indices that can be used to monitor and assess the impacts of climate change on different regions and sectors. Eight Precipitation indices were used for their study under four categories: duration, absolute, threshold, and percentile-based threshold indices. Table 2 provides specific descriptions of each of these indices. Climpact, an R-based software package was used to calculate the extreme precipitation indices. Further, the Mann–Kendall (MK) statistical test (Kendall, 1938) was used to assess the monotonic upward or downward trend, and Sen’s slope estimator (Sen, 1968) was used to calculate the magnitude of trend for seasonal, annual precipitation and extreme precipitation’s time series. We have also calculated the change in precipitation and extremes by dividing the study period into the 50s (2025–2055) and 80s (2056–2086) in reference to the historical period (1980-2014) for CMIP6 analysis. Spearman's Correlation (Spearman, 1961), a non-parametric statistical method, is employed to assess and quantify the relationships between observed precipitation and ENSO. Climpact is specifically designed to calculate climate indicators for various socio-economic sectors, such as health, agriculture, and water resources, utilizing daily temperature and rainfall data (Alexander & Herold, 2016). While it is preferable to employ extensive and complete instrumental observations as the primary data source, Climpact also accommodates the computation of indicators using alternative sources, including remote sensing data from satellites or reanalysis. In this study, Climpact was used to calculate extreme precipitation indices, demonstrating its versatility in climate research. Due to the missing precipitation data, climpact failed the quality control method. Bias correction was performed using CHIRPS dataset to fill the missing data for observed precipitation data. To address biases between station data and both the CHIRPS dataset and CMIP6 precipitation data quantile mapping was done, adopting the methodologies outlined by Piani et al., (2010) and Dosio & Paruolo, (2011) in this study and employing the techniques they proposed for bias correction of precipitation data. Quantile mapping was employed for bias correction utilizing the fitQmapPTF function in the R programming language, with additional support from the doQmapPTF function. This method involves fitting parametric transformations to the quantile-quantile relationship between observed and modeled precipitation values. The transformation function, represented by tfun, adjusts the distribution of model data to align with observed data. The wet day correction was implemented to equalize the fraction of days with precipitation between observed and modeled data. The formula for the exponential asymptotic transformation, used as one of the predefined transfun options, is where Po and Pm denote observed and modeled cumulative distribution functions, respectively a and b are coefficients determining the shape, while τ is a time constant influencing the rate at which the transformation approaches its asymptote. An anomalous occurrence was noted during the initial application of quantile mapping for the entire data period, as it failed to accurately capture the underlying equation for bias correction due to the high monthly variation in precipitation. To address this limitation, a refined approach was adopted, wherein the dataset was divided into individual monthly segments. Subsequently, bias correction was executed separately for each of the 12 months. This month-wise segmentation aimed to enhance the precision of the quantile mapping process, ensuring a more effective correction of biases within the data. Table 2 List of precipitation indices used Indices Indicator Name Definitions Units Duration indices CDD Consecutive dry days Maximum number of consecutive days with PR < 1 mm day CWD Consecutive wet days Maximum number of consecutive days with PR ≥1 mm day Absolute indices RX1 day Max 1-day precipitation Monthly maximum 1-day precipitation mm SDII Simple day intensity index Annual total precipitation divided by the number of wet days (defined as PRCP ≥ 0 mm) in the year mm/day PRCPTOT Annual total wet day precipitation Annual total PRCP in wet days (PR ≥ 1 mm) mm Threshold indices R10 Number of heavy precipitation days Annual count of days when PRCP ≥ 10 mm Day R30 Number of very heavy precipitation days Annual count of days when PRCP ≥ 30 mm Day Percentile-based threshold indices R95p Very wet days Annual total PRCP when PR > 95th percentile mm 3. Results and discussion 3.1 Extreme precipitation indices based on observed data The comprehensive analysis of precipitation indices from 1980 to 2022 in Nakhon Si Thammarat Province, southern Thailand, has several significant findings and provided insight on the region's changing precipitation patterns. Figure 2 shows a trend of observed precipitation indices time spanning from 1980 to 2022. Notably, a declining trend in consecutive dry days (CDD) with a slope of -0.18, but not statistically significant at a p-value of 0.26, suggests a potential decrease in dry periods, while a modest increase in consecutive wet days (CWD) with a slope of 0.07 and a p-value of 0.11 indicating a shift towards more frequent wet periods, carrying implications for flood management. The statistically significant upward trajectory in total precipitation (PRCPTOT) with a positive slope of 20.06 and a low p-value of 0.001 underscores a clear trend towards wetter conditions, influencing various sectors. Although daily precipitation intensity (SDII) displayed fluctuating patterns with a non-significant slope of 0.05 and a p-value of 0.21, indicating variability in intensity, a potential upward trend in extreme precipitation days (RX1 day) with a slope of 1.95 and a p-value of 0.08 suggests an increased frequency of intense precipitation events. Furthermore, heavy precipitation events (R10) and very heavy precipitation events (R30) exhibited statistically significant upward trends with slopes of 0.44 (p-value of 0.001) and 0.18 (p-value of 0.01), respectively, highlighting a clear intensification in extreme rainfall. The percentile-based threshold index (R95p) displayed variations with a positive slope of 7.21 and a p-value of 0.15, suggesting potential changes in the distribution of extreme precipitation events. Recent climate studies in Thailand indicate significant changes in climate indicators. Declining trend in consecutive dry days (CDD) aligns with a nationwide pattern, which satisfies with our result. Pratoomchai et al., (2020), finds that modest increase in consecutive wet days (CWD) which supports our results but contradicts regional trends noted by Amnuaylojaroen, (2021), emphasizing the regional variability in precipitation patterns. The upward trajectory in total precipitation (PRCPTOT) supports the overarching theme of increasing precipitation intensity observed by Limsakul & Singhruck (2016), as ours which suggests a general trend towards wetter conditions impacting various sectors. However, the fluctuating patterns of simple daily intensity index (SDII) is projected to rise by Ge et al., (2021), which satisfies with us, highlighting the complexity of daily precipitation intensity changes. This study contributes to the understanding of climate changes in Southern Thailand. The findings offer practical applications in areas such as water resource management, agriculture, and disaster preparedness. 3.2 Extreme precipitation indices based on CMIP6 dataset In our analysis of precipitation indices within Thailand's Nakhon Si Thammarat province, we have identified diverse trends of historical (1980–2014) and projected period (2015–2100) under SSP245 and SSP585 scenarios, as depicted in the right panel of Figure 3. The statistical results are summarized in the Table 3 below. For duration indices, the number of consecutive dry days (CDD) and consecutive wet days (CWD) remained relatively stable in both scenarios. Regarding absolute indices, total precipitation (PRCPTOT) displayed a significant upward trend in both SSP585 scenarios, with slope of 4.78mm, whereas SS245 scenarios has non-significant decreasing trend with slope -1.3mm. Max 1-day precipitation days (RX1 day) showed a potential upward trend in SSP585 with a slope of 0.27mm and a significant p-value of 0.043. For threshold indices, the number of heavy precipitation days (R10) and very heavy precipitation days (R30) displayed a slightly decreasing trend in SSP245, and a displayed a slightly increasing trend in SSP585 scenario. The simple daily intensity index (SDII) remained relatively stable in both scenarios. Lastly, the percentile-based threshold index (R95p) displayed a non-significant positive trend in SSP245, and a significant trend in SSP585 with slope 4.71 mm/day. These findings illustrate the complex nature of precipitation trends in the region, influenced by different emission scenarios, with SSP585 generally showing more pronounced changes compared to SSP245. Ge et al., (2021) analyzed 15 climate models (CMIP6) for Southeast Asia (SEA) indicates varying projections in precipitation extremes under different emission scenarios. Consecutive dry days (CDD) are expected to increase, especially in higher emission scenarios, which satisfies with our results, while consecutive wet days (CWD) are projected to decrease which contradicts with our results. Maximum 1-day precipitation (RX1 day) and total precipitation on wet days (PRCPTOT) show consistent increases across SEA(Ge et al., 2021), but our results shows an increase in only higher emission scenarios. Based on the probability distribution in the left panel of Figure 3, the duration of consecutive dry days similar for the 50s and 80s to the historical period, but the duration of consecutive wet days is higher for 80s with decreasing frequency (Fig 2b, d). The intensity of annual precipitation will increase in future for the 50s and 80s (Fig 2f). Intensity Max 1-day precipitation will increase in future for SSP585 scenarios (Fig 2h). Intensity of heavy and very heavy precipitation will increase in future with decreasing frequency, in case of the very heavy precipitation, intensity will be high for SSP585 80s as compared to other (Fig 2j, l). A significant difference was found for SDII with marginally higher intensity for 80s of SSP585 scenario than SSP245 and historical period (Fig 2p). Table 3 Statistical result of projected future trend of extreme indices based on ensemble mean of 6 CMIP6 models Index MK-Test Statistics CMIP6 Scenarios Historical SSP245 SSP585 CDD Sen’s Slope 0.053 0.064 0.164 p value 0.680 0.112 0.000 CWD Sen’s Slope 0.019 0.006 0.011 p value 0.691 0.687 0.531 PRCPTOT Sen’s Slope 5.017 -1.339 4.766 p value 0.334 0.403 0.023 RX1DAY Sen’s Slope 0.519 -0.005 0.277 p value 0.460 0.994 0.161 R10MM Sen’s Slope -0.010 -0.054 0.000 p value 0.921 0.062 0.959 R30MM Sen’s Slope 0.054 -0.017 0.065 p value 0.426 0.333 0.007 R95P Sen’s Slope 5.682 0.185 4.791 p value 0.132 0.924 0.001 SDII Sen’s Slope 0.030 0.003 0.049 p value 0.379 0.671 0.000 3.3 ENSO precipitation relationship To examine the fluctuation in the ENSO – extreme precipitation association in Nakhon Si Thammarat province, we computed the 15-year running correlations between the very wet days (R95p) and annual ENSO index (Fig. 4a) under different climate scenarios. In historical, SSP245 and SSP585, negative correlations were observed throughout the analysis period, indicating an inverse relationship between ENSO and R95p. This result is consistent with other studies by Curtis et al., (2007), Sun et al., (2015) and Xie et al., (2010), who similarly identified negative correlations between ENSO and extreme precipitation events in different regions. Specifically, correlations tended to be stronger in SSP585 compared to SSP245, suggesting a potential increase of this relationship under higher emission scenarios in this province. The stronger negative correlations observed under SSP585 indicate a possible increase of precipitation extremes under future climate scenarios characterized by higher greenhouse gas emissions. Additionally, correlations fluctuated over time, with periods of stronger negative correlations corresponding to significant ENSO events. The negative correlations between the ENSO and R95p indices imply that El Nino events are associated with suppressed extreme precipitation, while La Nina events coincide with enhanced extreme precipitation. Similar result was obtained by Kane, (1999), Kirtphaiboon et al., (2014), Kripalani & Kulkarni, (1997), Li & Ma, (2012), Sun et al., (2015) Wu et al., (2003) and Kirtphaiboon et al., (2014), that La Nina enhances the rainfall in different regions of Asia. This study contributes to a deeper understanding of the relationship between ENSO and extreme precipitation under different climate scenarios. The correlation analysis of ENSO and precipitation (Fig. 4) in Nakhon Si Thammarat province for different periods (Observed, Historical, 50s, and 80s) reveals varying degrees of influence under different emission scenarios. Firstly, the observed correlation coefficient of -0.5 underscores a strong negative relationship between ENSO and precipitation, suggesting a consistent pattern of lower precipitation during El Nino phases and higher precipitation during La Nina phases. The model depicts the observed association of rainfall and La Nina (negative correlation). In the high emission SSP585 scenario, weaker negative correlations is observed towards the end of 21 st century of weakended ENSO connection of precipitation, to the extend that such connection even would flip. Conversely, in the moderate SSP245 scenario and historical data, correlations stay lower, indicating that emission mitigation efforts help sustain the known ENSO connection with precipitation in the study area. 4. Conclusion This study highlights a significant upward trend in annual total precipitation in Southern Thailand's Nakhon Si Thammarat Province, signaling a shift towards a wetter climate. Monthly variations, particularly in November and February, are crucial for water resources and flood management with November experiencing significant increases in precipitation while February tends to be drier, hotter, and more prone to drought conditions. Extreme precipitation indices demonstrate varying patterns under different emission scenarios, suggesting increase in total precipitation (PRCPTOT) for both scenarios, a potential increase RX1 Day for SSP585. Specifically, extreme precipitation days (R95p) show an upward trend in SSP585 scenarios. The correlation analysis indicates a weaker relationship between ENSO and precipitation in Nakhon Si Thammarat province under the high emission SSP585 scenario compared to the moderate SSP245 scenario, indicating lower precipitation during El Nino phases and higher precipitation during La Nina phases. This finding suggests that projected excessive warming could hamper seasonal prediction of precipitation that relies on ENSO phases. Overall, this study provides essential insights for climate adaptation, disaster management, and sustainable development in Southern Thailand, emphasizing the need for a holistic approach to address the complexities of the climate system. Declarations Author Declarations: All authors have no conflicts of interest to declare. The manuscript has not been published or submitted for publication elsewhere. The authors declare that no funds were received during the preparation of this manuscript. The data used in the manuscript are included in the manuscript and all data are freely available. Author Contributions : All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Dipesh Kuinkel, and Khem Upreti. The first draft of the manuscript was written by Dipesh Kuinkel. and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. 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Trenberth, K. E., Branstator, G. W., Karoly, D., Kumar, A., Lau, N., & Ropelewski, C. (1998). Progress during TOGA in understanding and modeling global teleconnections associated with tropical sea surface temperatures. Journal of Geophysical Research: Oceans , 103 (C7), 14291–14324. https://doi.org/10.1029/97JC01444 Wang, X., Hou, X., & Wang, Y. (2017). Spatiotemporal variations and regional differences of extreme precipitation events in the Coastal area of China from 1961 to 2014. Atmospheric Research , 197 , 94–104. https://doi.org/10.1016/j.atmosres.2017.06.022 Westra, S., Alexander, L. V., & Zwiers, F. W. (2013). Global Increasing Trends in Annual Maximum Daily Precipitation. Journal of Climate , 26 (11), 3904–3918. https://doi.org/10.1175/JCLI-D-12-00502.1 Wieczorek, G. F., & Guzzetti, F. (1999). A review of rainfall thresholds for triggering landslides. Proc. of the EGS Plinius Conference, Maratea, Italy , 407–414. Wu, R., Hu, Z.-Z., & Kirtman, B. P. (2003). Evolution of ENSO-Related Rainfall Anomalies in East Asia. Journal of Climate , 16 (22), 3742–3758. https://doi.org/10.1175/1520-0442(2003)0162.0.CO;2 Xie, S.-P., Deser, C., Vecchi, G. A., Ma, J., Teng, H., & Wittenberg, A. T. (2010). Global Warming Pattern Formation: Sea Surface Temperature and Rainfall*. Journal of Climate , 23 (4), 966–986. https://doi.org/10.1175/2009JCLI3329.1 Zhang, X., Alexander, L., Hegerl, G. C., Jones, P., Tank, A. K., Peterson, T. C., Trewin, B., & Zwiers, F. W. (2011). Indices for monitoring changes in extremes based on daily temperature and precipitation data. WIREs Climate Change , 2 (6), 851–870. https://doi.org/10.1002/wcc.147 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4535815","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":315806326,"identity":"9383a0ee-1509-4a2c-a3e3-347190b17ed1","order_by":0,"name":"Dipesh Kuinkel","email":"","orcid":"","institution":"Tribhuvan University","correspondingAuthor":false,"prefix":"","firstName":"Dipesh","middleName":"","lastName":"Kuinkel","suffix":""},{"id":315806327,"identity":"d02137d3-6055-47f1-a738-4a7638749587","order_by":1,"name":"Parichart Promchote","email":"","orcid":"","institution":"Kasetsart University","correspondingAuthor":false,"prefix":"","firstName":"Parichart","middleName":"","lastName":"Promchote","suffix":""},{"id":315806328,"identity":"af489228-fee3-462a-b804-b29c722c96a1","order_by":2,"name":"Khem R. 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Simon Wang","email":"","orcid":"","institution":"Utah State University","correspondingAuthor":false,"prefix":"","firstName":"S.-Y.","middleName":"Simon","lastName":"Wang","suffix":""},{"id":315806330,"identity":"593cc8bf-e632-4869-b611-dc58a92ff668","order_by":4,"name":"Ngamindra Dahal","email":"","orcid":"","institution":"Nepal Water Conservation Foundation","correspondingAuthor":false,"prefix":"","firstName":"Ngamindra","middleName":"","lastName":"Dahal","suffix":""},{"id":315806331,"identity":"7f4f9ac0-8125-469a-9fde-94defe9c5e3f","order_by":5,"name":"Binod Pokharel","email":"data:image/png;base64,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","orcid":"","institution":"Tribhuvan University","correspondingAuthor":true,"prefix":"","firstName":"Binod","middleName":"","lastName":"Pokharel","suffix":""}],"badges":[],"createdAt":"2024-06-05 18:27:59","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4535815/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4535815/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00704-024-05150-y","type":"published","date":"2024-08-26T15:58:10+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":58625227,"identity":"14295b82-087b-435b-83f5-705fe939ac9e","added_by":"auto","created_at":"2024-06-19 04:23:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":777882,"visible":true,"origin":"","legend":"\u003cp\u003eStudy Area a) Map of Thailand showing Nakhon Si Thammarat province, b) Average monthly precipitation distribution c) Annual monthly distribution of observed precipitation from 1980-2022\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4535815/v1/78270b21766d253f7850553b.png"},{"id":58625230,"identity":"b0e1d03b-4cc8-410e-a1d1-c5bd282b996c","added_by":"auto","created_at":"2024-06-19 04:23:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":400485,"visible":true,"origin":"","legend":"\u003cp\u003eTrends of observed precipitation indices time spanning from 1980 to 2022.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4535815/v1/c1f2f443f7756ae20004f619.png"},{"id":58626031,"identity":"b619c4e3-0973-4c63-b6d0-636f67a8c93a","added_by":"auto","created_at":"2024-06-19 04:31:55","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":399893,"visible":true,"origin":"","legend":"\u003cp\u003eProjected future trend of extreme indices based on ensemble mean of 6 CMIP6 models, brown line indicates historical, green and red line indicates SSP245 and SSp585 scenario, shaded region is the inter model standard deviation. Left panel displays the probability density function plot for observed, historical and both scenarios for 50s and 80s.\u003c/p\u003e","description":"","filename":"3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4535815/v1/be9fbe88fead87eedeefb784.jpeg"},{"id":58625231,"identity":"211b5148-9014-4b4f-8c86-cf8771988fa2","added_by":"auto","created_at":"2024-06-19 04:23:55","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":181978,"visible":true,"origin":"","legend":"\u003cp\u003eRelation between ENSO and R95p a) 15- years running correlations between CMIP 6 ensemble means of R95p and ENSO, black line represents observed, brown line indicates historical, green and red line indicates SSP245 and SSp585 scenario, shaded region is the inter model standard deviation between the CMIP6 models. b) Correlations between CMIP6 ensemble means of R95p and ENSO for three different periods.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4535815/v1/5516051d5e124675e6ac6ea5.png"},{"id":63821536,"identity":"d2ed288b-44be-4b37-a141-75dd7210344d","added_by":"auto","created_at":"2024-09-02 16:14:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2132744,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4535815/v1/7cd5f569-bad9-406c-83cb-3171c94b175a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Changing Precipitation Patterns and Extremes in Southern Thailand under Warmer Climate","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eExtreme precipitation refers to unusually intense or prolonged heavy rainfall events that significantly exceed the average precipitation amounts for a given region (Curriero et al., 2001), leading to a stormflow or rapid accumulation of flood water. These events in addition to flooding, often cause landslides and pose threats to infrastructure, agriculture, and ecosystems (Du et al., 2022; Jiang et al., 2014; Nilsson \u0026amp; Svedmark, 2002; Ohl, 2000; Pregnolato et al., 2017; Rosenzweig et al., 2002; Wieczorek \u0026amp; Guzzetti, 1999). \u0026nbsp;Global trends in extreme precipitation show an overall increase in intensity and frequency in recent years (Alexander et al., 2006; Min et al., 2011; Solomon, 2007; Westra et al., 2013). Studies have observed a rising trend in extreme precipitation events across various regions (Karki et al., 2017; Kunkel, 2003; Stocker et al., 2014; Wang et al., 2017).\u003c/p\u003e\n\u003cp\u003eAsian countries, including Thailand, have undergone changes in precipitation patterns, affecting agriculture, water availability, and ecosystems (Hlophe-Ginindza \u0026amp; Mpandeli, 2020). Recent years have seen an increase in extreme precipitation events in Southern Thailand, leading to flash floods, landslides, and disruptions in local \u0026nbsp;(Endo et al., 2009; Limsakul \u0026amp; Singhruck, 2016). The evolving precipitation patterns have raised concerns about the region\u0026apos;s climate-related hazard vulnerability, necessitating effective adaptation strategies. Projections for future precipitation trends in Southern Thailand indicate a heightened likelihood of more intense and frequent extreme precipitation events (Arunrat et al., 2022). Climate models suggest that rising temperatures and shifting atmospheric circulation patterns will lead to shifts in monsoonal rainfall timing and intensity, affecting agricultural practices, water availability, and ecological systems (Seth et al., 2019). Additionally, sea-level rise and its interaction with extreme precipitation events pose added challenges for Southern Thailand\u0026apos;s coastal areas (Nicholls \u0026amp; Cazenave, 2010). Although climate models offer clues of future precipitation patterns\u0026nbsp;(Kattenberg et al., 1996),\u0026nbsp;these diverse outcomes underscore the complexity and regional variability inherent in climate projections, necessitating a comprehensive approach to understand and address the challenges posed by climate change.\u003c/p\u003e\n\u003cp\u003eThe El Nino\u0026ndash;Southern Oscillation (ENSO) phenomenon, characterized by variations in sea-surface temperature (SST) and the Southern Oscillation Index (SOI) in the equatorial Pacific Ocean, significantly influences global climate patterns. ENSO events, including El Nino and La Nina, have diverse impacts on precipitation and temperature worldwide (Taschetto et al., 2020). In Asia, particularly in Thailand, ENSO has been linked to various climatic phenomena. For instance, El Ni\u0026ntilde;o events often bring warm and dry conditions to Australia, Indonesia, and parts of South America, while La Ni\u0026ntilde;a events lead to cooler and wetter conditions in these regions (Kane, 1999; Lau \u0026amp; Wu, 2001; Ropelewski \u0026amp; Halpert, 1987; Trenberth et al., 1998). Understanding the dynamics of ENSO and its teleconnections is crucial for predicting and mitigating the socio-economic impacts of climate variability in Thailand and across Asia. As ENSO events can be predicted with some accuracy, quantifying their relationship with precipitation could enhance precipitation prediction capabilities (Amarasekera et al., 1997). Thus, investigating the linkages between ENSO and precipitation patterns is vital for improving climate forecasting and adaptation strategies in Thailand and other affected regions.\u003c/p\u003e\n\u003cp\u003eThis study aims to understand the historical and contemporary extreme precipitation patterns in Southern Thailand, with specific attention to Nakhon Si Thammarat Province (Figure 1a). The investigation incorporates climate models precipitation data to project future changes, considering various scenarios and providing valuable insights into potential climate impacts on the region. Additionally, the study explores the influence of ENSO on extreme precipitation in Southern Thailand, aiming to contribute essential knowledge for effective climate change mitigation and adaptation planning in the region.\u003c/p\u003e"},{"header":"2.\tMaterial and Methods","content":"\u003ch2\u003e2.1 Study Area\u003c/h2\u003e\n\u003cp\u003eNakhon Si Thammarat, a southern province in Thailand, flooding is a recurrent event affecting the entire province (Langkulsen et al., 2022). Every year (Feb 2017, Jan 2017, Dec 2018, Dec 2020, Dec 2021, Dec 2022, Feb 2022, Nov 2023, etc ), \u0026nbsp;it causes lives and damages to infrastructure, agricultural production and severely affects local economic development (Centre for Research on the Epidemiology of Disasters (CRED), 2023). This province has been selected as the focus area of our research to tackle challenges associated with extreme precipitation and flooding and to empower policymakers and stakeholders to make informed decisions to enhance resilience and adaptability to a changing climate.\u003c/p\u003e\n\u003cp\u003eNakhon Si Thammarat province is characterized by a diverse topography and climate. Bordered by the Gulf of Thailand to the east and the Andaman Sea to the west, this province exhibits a range of elevations. The topography includes low-lying coastal areas along the Gulf of Thailand, which are close to sea level, as well as higher terrain in the central and western regions, reaching elevations of approximately 1800 meters above sea level. The seasonal distribution of rainfall plays a crucial role in shaping the landscape, ecosystems, and overall environmental conditions in Nakhon Si Thammarat, contributing to the region\u0026apos;s unique climatic characteristics.\u003c/p\u003e\n\u003cp\u003eThe climate in this region is tropical, typical of Thailand, with distinct variations throughout the year. The average temperatures range from 25\u0026deg;C to 32\u0026deg;C. The province experiences a tropical climate, with warm temperatures prevailing throughout the year. The mean annual precipitation is around 2400 mm. The annual rainfall pattern in Nakhon Si Thammarat displays distinct seasonal variations, with the highest precipitation occurring in October, November, December, and January (ONDJ), collectively contributing to over 60% of the total annual rainfall. In contrast, February experiences the lowest rainfall, representing a drier phase in the climate cycle (Figure 1b). Noteworthy changes in the annual rainfall pattern has been observed, particularly from the year 2000 onwards. There is a discernible upward trend in precipitation during January (Figure 1c). This shift in historical precipitation patterns is significant, indicating an increase in January rainfall compared to previous years.\u003c/p\u003e\n\u003ch2\u003e2.2 Datasets\u003c/h2\u003e\n\u003cp\u003eDaily observed precipitation data from the Nakhon Si Thammarat Province station (552201), obtained from the Thai Meteorological Department (TMD), was used for this study. The station data covered the period from 1980 to 2022. Additionally, the daily precipitation data from Climate Hazards Group Infrared Precipitation with Station (CHIRPS) was acquired from the Climate Engine app (https://app.climateengine.com), covering the period 1981 to 2022. The CHIRPS data was bias-corrected using the station data and it was used to fill the missing data of the observed station data.\u003c/p\u003e\n\u003cp\u003eIn our study focusing on extreme precipitation and its trends as proposed by Expert Team on Climate Change Detection and Indices (ETCCTI) in Nakhon Si Thammarat province, Thailand, we employed the NASA Earth Exchange Global Daily Downscaled Projections archive (NEX-GDDP-CMIP-6). NEX-GDDP-CMIP6 dataset, which serves as a bias-corrected downscaled iteration of Global Climate Models (GCM) (Thrasher et al., 2022). To assess precipitation and its extreme variability in southern Thailand, we obtained the bias-corrected versions of 6 CMIP6 model experiments from source, https://www.nccs.nasa.gov/services/data-collections/land-based-products/nex-gddp-cmip6. These 6 models were also employed by Rojpratak \u0026amp; Supharatid, (2023); Ge et al., (2021); De Silva et al., (2023) and Supharatid et al., (2022) \u0026nbsp;in their previous study focusing on the Southeast Asia (SEA) and Thailand region. Similarly, the NINO3.4 data for same 6 CMIP6 model was obtained from Climate Variability Diagnostics Package (CVDP) (Phillips et al., 2014) from https://www2.cesm.ucar.edu/working_groups/CVC/cvdp/data-repository.html to analyze the relationship between ENSO and annual precipitation. Our investigation spanned historical simulations covering the period from 1980 to 2014 and projected periods extending from 2015 to 2100 under two Shared Socioeconomic Pathways (SSPs): SSP245 and SSP585. The SSP245 (SSP585) is an update of the CMIP5 scenario RCP4.5 (RCP8.5), an additional radiative forcing of 4.5 (8.5) W/m\u0026sup2; by the year 2100, with now combined with socioeconomic reasons in CMIP6 future scenario (Thrasher et al., 2022). As, these scenarios form an essential part of climate change study representing the medium (SSP245) and high (SSP585) pathways of future greenhouse gas emissions. Precipitation data was again bias-corrected with the observed precipitation data and further analysis was performed. Table 1 provides a detailed list of the CMIP6 ensemble members employed in our study, thus forming the foundation for our investigation into precipitation patterns, ENSO relationship and extreme variability in the study region.\u003c/p\u003e\n\u003cp\u003eTable 1 List of Bias-corrected CMIP6 models used\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.173076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eS.N.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.634615384615383%\" valign=\"top\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"70.1923076923077%\" valign=\"top\"\u003e\n \u003cp\u003eName\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.173076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.634615384615383%\" valign=\"top\"\u003e\n \u003cp\u003eACCESS-CM2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"70.1923076923077%\" valign=\"top\"\u003e\n \u003cp\u003eAustralian Community Climate and Earth System Simulator - Climate Model version 2 (Australia)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.173076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.634615384615383%\" valign=\"top\"\u003e\n \u003cp\u003eACCESS-ESM1-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"70.1923076923077%\" valign=\"top\"\u003e\n \u003cp\u003eAustralian Community Climate and Earth System Simulator - Earth System Model version 1.5 (Australia)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.173076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.634615384615383%\" valign=\"top\"\u003e\n \u003cp\u003eEC EARTH 3CC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"70.1923076923077%\" valign=\"top\"\u003e\n \u003cp\u003eEC-Earth Climate Model version 3 Coupled Configuration (Europe)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.173076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.634615384615383%\" valign=\"top\"\u003e\n \u003cp\u003eINM-CM5-0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"70.1923076923077%\" valign=\"top\"\u003e\n \u003cp\u003eInstitute of Numerical Mathematics Climate Model version 5.0 (Russia)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.173076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.634615384615383%\" valign=\"top\"\u003e\n \u003cp\u003eMIROC-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"70.1923076923077%\" valign=\"top\"\u003e\n \u003cp\u003eModel for Interdisciplinary Research on Climate version 6 (Japan)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.173076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.634615384615383%\" valign=\"top\"\u003e\n \u003cp\u003eFGOALS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"70.1923076923077%\" valign=\"top\"\u003e\n \u003cp\u003eFGOALS Model (China)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch2\u003e2.3 Approach to analysis\u003c/h2\u003e\n\u003cp\u003eApproach to analysis of various model outputs and datasets has been adopted from similar exercises undertaken before. For example, the types of extreme precipitation indices proposed by Zhang et al. (2011) as part of the Expert Team on Climate Change Detection and Indices (ETCCTI) offer ways to interpret the patterns. The ETCCTI is a group of climate experts convened by the World Meteorological Organization (WMO) with the goal of developing a suite of climate change indices that can be used to monitor and assess the impacts of climate change on different regions and sectors. Eight Precipitation indices were used for their study under four categories: duration, absolute, threshold, and percentile-based threshold indices. Table 2 provides specific descriptions of each of these indices. Climpact, an R-based software package was used to calculate the extreme precipitation indices.\u003c/p\u003e\n\u003cp\u003eFurther, the Mann\u0026ndash;Kendall (MK) statistical test (Kendall, 1938) was used to assess the monotonic upward or downward trend, and Sen\u0026rsquo;s slope estimator (Sen, 1968) was used to calculate the magnitude of trend for seasonal, annual precipitation and extreme precipitation\u0026rsquo;s time series. We have also calculated the change in precipitation and extremes by dividing the study period into the 50s (2025\u0026ndash;2055) and 80s (2056\u0026ndash;2086) in reference to the historical period (1980-2014) for CMIP6 analysis. Spearman\u0026apos;s Correlation (Spearman, 1961), a non-parametric statistical method, is employed to assess and quantify the relationships between observed precipitation and ENSO.\u003c/p\u003e\n\u003cp\u003eClimpact is specifically designed to calculate climate indicators for various socio-economic sectors, such as health, agriculture, and water resources, utilizing daily temperature and rainfall data (Alexander \u0026amp; Herold, 2016). While it is preferable to employ extensive and complete instrumental observations as the primary data source, Climpact also accommodates the computation of indicators using alternative sources, including remote sensing data from satellites or reanalysis. In this study, Climpact was used to calculate extreme precipitation indices, demonstrating its versatility in climate research. Due to the missing precipitation data, climpact failed the quality control method. Bias correction was performed using CHIRPS dataset to fill the missing data for observed precipitation data.\u003c/p\u003e\n\u003cp\u003eTo address biases between station data and both the CHIRPS dataset and CMIP6 precipitation data quantile mapping was done, adopting the methodologies outlined by Piani et al., (2010) and Dosio \u0026amp; Paruolo, (2011) in this study and employing the techniques they proposed for bias correction of precipitation data. Quantile mapping was employed for bias correction utilizing the fitQmapPTF function in the R programming language, with additional support from the doQmapPTF function. This method involves fitting parametric transformations to the quantile-quantile relationship between observed and modeled precipitation values. The transformation function, represented by tfun, adjusts the distribution of model data to align with observed data. The wet day correction was implemented to equalize the fraction of days with precipitation between observed and modeled data. The formula for the exponential asymptotic transformation, used as one of the predefined transfun options, is\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"439\" height=\"49\"\u003e\u003c/p\u003e\n\u003cp\u003ewhere Po and Pm denote observed and modeled cumulative distribution functions, respectively a and b are coefficients determining the shape, while \u0026tau; is a time constant influencing the rate at which the transformation approaches its asymptote.\u003c/p\u003e\n\u003cp\u003eAn anomalous occurrence was noted during the initial application of quantile mapping for the entire data period, as it failed to accurately capture the underlying equation for bias correction due to the high monthly variation in precipitation. To address this limitation, a refined approach was adopted, wherein the dataset was divided into individual monthly segments. Subsequently, bias correction was executed separately for each of the 12 months. This month-wise segmentation aimed to enhance the precision of the quantile mapping process, ensuring a more effective correction of biases within the data.\u003c/p\u003e\n\u003cp\u003eTable 2 \u0026nbsp;List of precipitation indices used\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"571\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.614035087719298%\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndices\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.87719298245614%\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndicator Name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.92982456140351%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDefinitions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnits\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eDuration indices\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.614035087719298%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCDD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.87719298245614%\"\u003e\n \u003cp\u003eConsecutive dry days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.92982456140351%\"\u003e\n \u003cp\u003eMaximum number of consecutive days with PR \u0026lt; 1 mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003eday\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.614035087719298%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCWD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.87719298245614%\"\u003e\n \u003cp\u003eConsecutive wet days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.92982456140351%\"\u003e\n \u003cp\u003eMaximum number of consecutive days with PR \u0026ge;1 mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003eday\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbsolute indices\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.614035087719298%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRX1 day\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.87719298245614%\"\u003e\n \u003cp\u003eMax 1-day precipitation\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.92982456140351%\"\u003e\n \u003cp\u003eMonthly maximum 1-day precipitation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003emm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.614035087719298%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSDII\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.87719298245614%\"\u003e\n \u003cp\u003eSimple day intensity index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.92982456140351%\"\u003e\n \u003cp\u003eAnnual total precipitation divided by the number of wet days (defined as PRCP \u0026nbsp; \u0026nbsp; \u0026ge; 0 mm) in the year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003emm/day\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.614035087719298%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePRCPTOT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.87719298245614%\"\u003e\n \u003cp\u003eAnnual total wet day precipitation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.92982456140351%\"\u003e\n \u003cp\u003eAnnual total PRCP in wet days (PR \u0026ge; 1 mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003emm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eThreshold indices\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.614035087719298%\"\u003e\n \u003cp\u003e\u003cstrong\u003eR10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.87719298245614%\"\u003e\n \u003cp\u003eNumber of heavy precipitation days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.92982456140351%\"\u003e\n \u003cp\u003eAnnual count of days when PRCP \u0026ge; 10 mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003eDay\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.614035087719298%\"\u003e\n \u003cp\u003e\u003cstrong\u003eR30\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.87719298245614%\"\u003e\n \u003cp\u003eNumber of very \u0026nbsp;heavy \u0026nbsp; \u0026nbsp; precipitation days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.92982456140351%\"\u003e\n \u003cp\u003eAnnual count of days when PRCP \u0026ge; 30 mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003eDay\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentile-based threshold \u0026nbsp;indices\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.614035087719298%\"\u003e\n \u003cp\u003e\u003cstrong\u003eR95p\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.87719298245614%\"\u003e\n \u003cp\u003eVery wet days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.92982456140351%\"\u003e\n \u003cp\u003eAnnual total PRCP when PR \u0026gt; 95th percentile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003emm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n"},{"header":"3.\tResults and discussion","content":"\u003ch2\u003e3.1 Extreme precipitation indices based on observed data\u003c/h2\u003e\n\u003cp\u003eThe comprehensive analysis of precipitation indices from 1980 to 2022 in Nakhon Si Thammarat Province, southern Thailand, has several significant findings and provided insight on the region\u0026apos;s changing precipitation patterns. Figure 2 shows a trend of observed precipitation indices time spanning from 1980 to 2022. Notably, a declining trend in consecutive dry days (CDD) with a slope of -0.18, but not statistically significant at a p-value of 0.26, suggests a potential decrease in dry periods, while a modest increase in consecutive wet days (CWD) with a slope of 0.07 and a p-value of 0.11 indicating a shift towards more frequent wet periods, carrying implications for flood management. The statistically significant upward trajectory in total precipitation (PRCPTOT) with a positive slope of 20.06 and a low p-value of 0.001 underscores a clear trend towards wetter conditions, influencing various sectors. Although daily precipitation intensity (SDII) displayed fluctuating patterns with a non-significant slope of 0.05 and a p-value of 0.21, indicating variability in intensity, a potential upward trend in extreme precipitation days (RX1 day) with a slope of 1.95 and a p-value of 0.08 suggests an increased frequency of intense precipitation events. Furthermore, heavy precipitation events (R10) and very heavy precipitation events (R30) exhibited statistically significant upward trends with slopes of 0.44 (p-value of 0.001) and 0.18 (p-value of 0.01), respectively, highlighting a clear intensification in extreme rainfall. The percentile-based threshold index (R95p) displayed variations with a positive slope of 7.21 and a p-value of 0.15, suggesting potential changes in the distribution of extreme precipitation events.\u003c/p\u003e\n\u003cp\u003eRecent climate studies in Thailand indicate significant changes in climate indicators. Declining trend in consecutive dry days (CDD) aligns with a nationwide pattern, which satisfies with our result. Pratoomchai et al., (2020), finds that \u0026nbsp;modest increase in consecutive wet days (CWD) which supports our results but \u0026nbsp;contradicts regional trends noted by Amnuaylojaroen, (2021), emphasizing the regional variability in precipitation patterns. The upward trajectory in total precipitation (PRCPTOT) supports the overarching theme of increasing precipitation intensity observed by Limsakul \u0026amp; Singhruck (2016), as ours which suggests a general trend towards wetter conditions impacting various sectors. However, the fluctuating patterns of simple daily intensity index (SDII) is projected to rise by Ge et al., (2021), which satisfies with us, highlighting the complexity of daily precipitation intensity changes. This study contributes to the understanding of climate changes in Southern Thailand. The findings offer practical applications in areas such as water resource management, agriculture, and disaster preparedness.\u003c/p\u003e\n\u003ch2\u003e3.2 Extreme precipitation indices based on CMIP6 dataset\u003c/h2\u003e\n\u003cp\u003eIn our analysis of precipitation indices within Thailand\u0026apos;s Nakhon Si Thammarat province, we have identified diverse trends of historical (1980\u0026ndash;2014) and projected period (2015\u0026ndash;2100) under SSP245 and SSP585 scenarios, as depicted in the right panel of \u0026nbsp;Figure 3. The statistical results are summarized in the Table 3 below. For duration indices, the number of consecutive dry days (CDD) and consecutive wet days (CWD) remained relatively stable in both scenarios. Regarding absolute indices, total precipitation (PRCPTOT) displayed a significant upward trend in both SSP585 scenarios, with slope of 4.78mm, whereas SS245 scenarios has non-significant decreasing trend with slope -1.3mm. Max 1-day precipitation days (RX1 day) showed a potential upward trend in SSP585 with a slope of 0.27mm and a significant p-value of 0.043. \u0026nbsp;For threshold indices, the number of heavy precipitation days (R10) and very heavy precipitation days (R30) displayed a slightly decreasing trend in SSP245, and a displayed a slightly increasing trend in SSP585 scenario. The simple daily intensity index (SDII) remained relatively stable in both scenarios. Lastly, the percentile-based threshold index (R95p) displayed a non-significant positive trend in SSP245, and a significant trend in SSP585 with slope 4.71 mm/day. These findings illustrate the complex nature of precipitation trends in the region, influenced by different emission scenarios, with SSP585 generally showing more pronounced changes compared to SSP245. Ge et al., (2021) analyzed 15 climate models (CMIP6) for Southeast Asia (SEA) indicates varying projections in precipitation extremes under different emission scenarios. Consecutive dry days (CDD) are expected to increase, especially in higher emission scenarios, which satisfies with our results, while consecutive wet days (CWD) are projected to decrease which contradicts with our results. Maximum 1-day precipitation (RX1 day) and total precipitation on wet days (PRCPTOT) show consistent increases across SEA(Ge et al., 2021), but our results shows an increase in only higher emission scenarios.\u003c/p\u003e\n\u003cp\u003eBased on the probability distribution in the left panel of Figure 3, the duration of consecutive dry days similar for the 50s and 80s to the historical period, but the duration of consecutive wet days is higher for 80s with decreasing frequency (Fig 2b, d). The intensity of annual precipitation will increase in future for the 50s and 80s (Fig 2f). Intensity Max 1-day precipitation will increase in future for SSP585 scenarios (Fig 2h). Intensity of heavy and very heavy precipitation will increase in future with decreasing frequency, in case of the very heavy precipitation, intensity will be high for SSP585 80s as compared to other (Fig 2j, l). A significant difference was found for SDII with marginally higher intensity for 80s of SSP585 scenario than SSP245 and historical period (Fig 2p).\u003c/p\u003e\n\u003cp\u003eTable 3 \u0026nbsp;Statistical result of projected future trend of extreme indices based on ensemble mean of 6 CMIP6 models\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"482\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.236514522821576%\" rowspan=\"2\"\u003e\n \u003cp\u003eIndex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.70954356846473%\" rowspan=\"2\"\u003e\n \u003cp\u003eMK-Test Statistics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"57.053941908713696%\" colspan=\"3\"\u003e\n \u003cp\u003eCMIP6 Scenarios\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.22627737226277%\"\u003e\n \u003cp\u003eHistorical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.386861313868614%\"\u003e\n \u003cp\u003eSSP245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.386861313868614%\"\u003e\n \u003cp\u003eSSP585\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.284823284823286%\" rowspan=\"2\"\u003e\n \u003cp\u003eCDD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.75051975051975%\"\u003e\n \u003cp\u003eSen\u0026rsquo;s Slope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.205821205821206%\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.87941787941788%\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.87941787941788%\"\u003e\n \u003cp\u003e0.164\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.745257452574524%\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.642276422764226%\"\u003e\n \u003cp\u003e0.680\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.306233062330623%\"\u003e\n \u003cp\u003e0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.306233062330623%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.284823284823286%\" rowspan=\"2\"\u003e\n \u003cp\u003eCWD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.75051975051975%\"\u003e\n \u003cp\u003eSen\u0026rsquo;s Slope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.205821205821206%\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.87941787941788%\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.87941787941788%\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.745257452574524%\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.642276422764226%\"\u003e\n \u003cp\u003e0.691\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.306233062330623%\"\u003e\n \u003cp\u003e0.687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.306233062330623%\"\u003e\n \u003cp\u003e0.531\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.284823284823286%\" rowspan=\"2\"\u003e\n \u003cp\u003ePRCPTOT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.75051975051975%\"\u003e\n \u003cp\u003eSen\u0026rsquo;s Slope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.205821205821206%\"\u003e\n \u003cp\u003e5.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.87941787941788%\"\u003e\n \u003cp\u003e-1.339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.87941787941788%\"\u003e\n \u003cp\u003e4.766\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.745257452574524%\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.642276422764226%\"\u003e\n \u003cp\u003e0.334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.306233062330623%\"\u003e\n \u003cp\u003e0.403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.306233062330623%\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.284823284823286%\" rowspan=\"2\"\u003e\n \u003cp\u003eRX1DAY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.75051975051975%\"\u003e\n \u003cp\u003eSen\u0026rsquo;s Slope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.205821205821206%\"\u003e\n \u003cp\u003e0.519\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.87941787941788%\"\u003e\n \u003cp\u003e-0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.87941787941788%\"\u003e\n \u003cp\u003e0.277\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.745257452574524%\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.642276422764226%\"\u003e\n \u003cp\u003e0.460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.306233062330623%\"\u003e\n \u003cp\u003e0.994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.306233062330623%\"\u003e\n \u003cp\u003e0.161\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.284823284823286%\" rowspan=\"2\"\u003e\n \u003cp\u003eR10MM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.75051975051975%\"\u003e\n \u003cp\u003eSen\u0026rsquo;s Slope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.205821205821206%\"\u003e\n \u003cp\u003e-0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.87941787941788%\"\u003e\n \u003cp\u003e-0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.87941787941788%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.745257452574524%\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.642276422764226%\"\u003e\n \u003cp\u003e0.921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.306233062330623%\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.306233062330623%\"\u003e\n \u003cp\u003e0.959\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.284823284823286%\" rowspan=\"2\"\u003e\n \u003cp\u003eR30MM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.75051975051975%\"\u003e\n \u003cp\u003eSen\u0026rsquo;s Slope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.205821205821206%\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.87941787941788%\"\u003e\n \u003cp\u003e-0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.87941787941788%\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.745257452574524%\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.642276422764226%\"\u003e\n \u003cp\u003e0.426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.306233062330623%\"\u003e\n \u003cp\u003e0.333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.306233062330623%\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.284823284823286%\" rowspan=\"2\"\u003e\n \u003cp\u003eR95P\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.75051975051975%\"\u003e\n \u003cp\u003eSen\u0026rsquo;s Slope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.205821205821206%\"\u003e\n \u003cp\u003e5.682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.87941787941788%\"\u003e\n \u003cp\u003e0.185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.87941787941788%\"\u003e\n \u003cp\u003e4.791\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.745257452574524%\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.642276422764226%\"\u003e\n \u003cp\u003e0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.306233062330623%\"\u003e\n \u003cp\u003e0.924\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.306233062330623%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.284823284823286%\" rowspan=\"2\"\u003e\n \u003cp\u003eSDII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.75051975051975%\"\u003e\n \u003cp\u003eSen\u0026rsquo;s Slope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.205821205821206%\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.87941787941788%\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.87941787941788%\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.745257452574524%\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.642276422764226%\"\u003e\n \u003cp\u003e0.379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.306233062330623%\"\u003e\n \u003cp\u003e0.671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.306233062330623%\"\u003e\n \u003cp\u003e0.000\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\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e3.3 ENSO precipitation relationship\u003c/h2\u003e\n\u003cp\u003eTo examine the fluctuation in the ENSO \u0026ndash; extreme precipitation association in Nakhon Si Thammarat province, we computed the 15-year running correlations between the very wet days (R95p) and annual ENSO index (Fig. 4a) under different climate scenarios. In historical, SSP245 and SSP585, negative correlations were observed throughout the analysis period, indicating an inverse relationship between ENSO and R95p. This result is consistent with other studies by Curtis et al., (2007), Sun et al., (2015) and Xie et al., (2010), \u0026nbsp;who similarly identified negative correlations between ENSO and extreme precipitation events in different regions. Specifically, correlations tended to be stronger in SSP585 compared to SSP245, suggesting a potential increase of this relationship under higher emission scenarios in this province. The stronger negative correlations observed under SSP585 indicate a possible increase of precipitation extremes under future climate scenarios characterized by higher greenhouse gas emissions. Additionally, correlations fluctuated over time, with periods of stronger negative correlations corresponding to significant ENSO events. The negative correlations between the ENSO and R95p indices imply that El Nino events are associated with suppressed extreme precipitation, while La Nina events coincide with enhanced extreme precipitation. Similar result was obtained by Kane, (1999), Kirtphaiboon et al., (2014), Kripalani \u0026amp; Kulkarni, (1997), Li \u0026amp; Ma, (2012), Sun et al., (2015) \u0026nbsp;Wu et al., (2003) and Kirtphaiboon et al., (2014), that La Nina enhances the rainfall in different regions of Asia. This study contributes to a deeper understanding of the relationship between ENSO and extreme precipitation under different climate scenarios.\u003c/p\u003e\n\u003cp\u003eThe correlation analysis of ENSO and precipitation (Fig. 4) in Nakhon Si Thammarat province for different periods (Observed, Historical, 50s, and 80s) reveals varying degrees of influence under different emission scenarios. Firstly, the observed correlation coefficient of -0.5 underscores a strong negative relationship between ENSO and precipitation, suggesting a consistent pattern of lower precipitation during El Nino phases and higher precipitation during La Nina phases. The model depicts the observed association of rainfall and La Nina (negative correlation). \u0026nbsp;In the high emission SSP585 scenario, weaker negative correlations is observed towards the end of 21\u003csup\u003est\u003c/sup\u003e century of weakended ENSO connection of precipitation, to the extend that such connection even would flip. Conversely, in the moderate SSP245 scenario and historical data, correlations stay lower, indicating that emission mitigation efforts help sustain the known ENSO connection with precipitation in the study area. \u0026nbsp;\u003c/p\u003e"},{"header":"4.\tConclusion","content":"\u003cp\u003eThis study highlights a significant upward trend in annual total precipitation in Southern Thailand\u0026apos;s Nakhon Si Thammarat Province, signaling a shift towards a wetter climate. Monthly variations, particularly in November and February, are crucial for water resources and flood management with \u0026nbsp;November experiencing significant increases in precipitation while February tends to be drier, hotter, and more prone to drought conditions. Extreme precipitation indices demonstrate varying patterns under different emission scenarios, suggesting increase in total precipitation (PRCPTOT) for both scenarios, a potential increase RX1 Day for SSP585. Specifically, extreme precipitation days (R95p) show an upward trend in SSP585 scenarios. The correlation analysis indicates a weaker relationship between ENSO and precipitation in Nakhon Si Thammarat province under the high emission SSP585 scenario compared to the moderate SSP245 scenario, indicating lower precipitation during El Nino phases and higher precipitation during La Nina phases. This finding suggests that projected excessive warming could hamper seasonal prediction of precipitation that relies on ENSO phases. Overall, this study provides essential insights for climate adaptation, disaster management, and sustainable development in Southern Thailand, emphasizing the need for a holistic approach to address the complexities of the climate system.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Declarations:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have no conflicts of interest to declare. The manuscript has not been published or submitted for publication elsewhere. The authors declare that no funds were received during the preparation of this manuscript. The data used in the manuscript are included in the manuscript and all data are freely available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Dipesh Kuinkel, and Khem Upreti. The first draft of the manuscript was written by Dipesh Kuinkel. and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAlexander, L., \u0026amp; Herold, N. (2016). \u003cem\u003eClimPACT2: Indices and software\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eAlexander, L. V., Zhang, X., Peterson, T. 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Indices for monitoring changes in extremes based on daily temperature and precipitation data. \u003cem\u003eWIREs Climate Change\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e(6), 851\u0026ndash;870. https://doi.org/10.1002/wcc.147\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-climatology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taac","sideBox":"Learn more about [Theoretical and Applied Climatology](https://www.springer.com/journal/704)","snPcode":"704","submissionUrl":"https://submission.nature.com/new-submission/704/3","title":"Theoretical and Applied Climatology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"CMIP6, Precipitation, Extreme, ENSO, Southern Thailand","lastPublishedDoi":"10.21203/rs.3.rs-4535815/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4535815/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Southern Thailand has experienced significant shifts in precipitation patterns in recent years, exerting substantial impacts on regional water resources and infrastructure systems. This study aims to elucidate these changes and underlying factors based on daily precipitation observations from Nakhon Si Thammarat Province spanning 1980 to 2022. Additionally, data from the Coupled Model Intercomparison Project Phase 6 (CMIP6) is utilized to investigate projected changes in precipitation for 2015-2100 relative to the historical period (1980-2014), employing a comprehensive analysis considering two emissions scenarios (SSP245 and SSP585) across six models. Various precipitation indices are selected to assess trends and statistical significance using the Mann-Kendall test.\nBoth observed and climate model data indicate an increasing precipitation trend in Southern Thailand, with a reduced association with the El Niño-Southern Oscillation (ENSO) under warming conditions. Extreme precipitation indices also exhibit an increasing trend, with total precipitation and the 95th percentile of daily precipitation (R95p) revealing very wet conditions in recent years, projected to continue increasing. Contrastingly, the number of dry days is also mounting, suggesting that both dry and wet extremes will impact Southern Thailand under a warmer climate. The findings from this study provide an early indication of future precipitation and extreme event scenarios, which can inform the development of measures to mitigate climate change-related hazards in the region.","manuscriptTitle":"Changing Precipitation Patterns and Extremes in Southern Thailand under Warmer Climate","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-19 04:23:51","doi":"10.21203/rs.3.rs-4535815/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"179446139794058132677169304154155179504","date":"2024-06-18T08:31:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"142450786247196520334943202823142016829","date":"2024-06-17T03:24:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"244941287667536709341173683660140186905","date":"2024-06-17T01:19:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"163767414253644551889254726083503411352","date":"2024-06-17T01:03:56+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-16T06:29:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-05T23:38:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-05T23:36:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"Theoretical and Applied Climatology","date":"2024-06-05T18:26:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-climatology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taac","sideBox":"Learn more about [Theoretical and Applied Climatology](https://www.springer.com/journal/704)","snPcode":"704","submissionUrl":"https://submission.nature.com/new-submission/704/3","title":"Theoretical and Applied Climatology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"486c7a0c-90da-46a9-9e9f-90a247e62bd2","owner":[],"postedDate":"June 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-09-02T16:08:46+00:00","versionOfRecord":{"articleIdentity":"rs-4535815","link":"https://doi.org/10.1007/s00704-024-05150-y","journal":{"identity":"theoretical-and-applied-climatology","isVorOnly":false,"title":"Theoretical and Applied Climatology"},"publishedOn":"2024-08-26 15:58:10","publishedOnDateReadable":"August 26th, 2024"},"versionCreatedAt":"2024-06-19 04:23:51","video":"","vorDoi":"10.1007/s00704-024-05150-y","vorDoiUrl":"https://doi.org/10.1007/s00704-024-05150-y","workflowStages":[]},"version":"v1","identity":"rs-4535815","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4535815","identity":"rs-4535815","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}