Climate Extreme Indices Research Articles

Trends and comparison of climate extreme indices in coastal locations under similar Köppen–Geiger classification using linear modelling approach

Trends and comparison of climate extreme indices in coastal locations under similar Köppen–Geiger classification using linear modelling approach

Evaluating the Impact of Future Seasonal Climate Extremes on Crop Evapotranspiration of Maize in Western Kansas Using a Machine Learning Approach

Data-driven technologies are employed in agriculture to optimize the use of limited resources. Crop evapotranspiration (ET) estimates the actual amount of water that crops require at different growth stages, thereby proving to be the essential information needed for precision irrigation. Crop ET is essential in areas like the US High Plains, where farmers rely on groundwater for irrigation. The sustainability of irrigated agriculture in the region is threatened by diminishing groundwater levels, and the increasing frequency of extreme events caused by climate change further exacerbates the situation. These conditions can significantly affect crop ET rates, leading to water stress, which adversely affects crop yields. In this study, we analyze historical climate data using a machine learning model to determine which of the climate extreme indices most influences crop ET. Crop ET is estimated using reference ET derived from the FAO Penman–Monteith equation, which is multiplied with the crop coefficient data estimated from the remotely sensed normalized difference vegetation index (NDVI). We found that the climate extreme indices of consecutive dry days and the mean weekly maximum temperatures most influenced crop ET. It was found that temperature-derived indices influenced crop ET more than precipitation-derived indices. Under the future climate scenarios, we predict that crop ET will increase by 0.4% and 1.7% in the near term, by 3.1% and 5.9% in the middle term, and by 3.8% and 9.6% at the end of the century under low greenhouse gas emission and high greenhouse gas emission scenarios, respectively. These predicted changes in seasonal crop ET can help agricultural producers to make well-informed decisions to optimize groundwater resources.

Impact of bias correction on climate change signals over central Europe and the Iberian Peninsula

Vegetation models for climate adaptation and mitigation strategies require spatially high-resolution climate input data in which the error with respect to observations has been previously corrected. To quantify the impact of bias correction, we examine the effects of quantile-mapping bias correction on the climate change signal (CCS) of climate, extremes, and biological variables from the convective regional climate model COSMO-CLM and two dynamic vegetation models (LPJ-GUESS and CARAIB). COSMO-CLM was driven and analyzed at 3 km horizontal resolution over Central Europe (CE) and the Iberian Peninsula (IP) for the transient period 1980–2070 under the RCP8.5 scenario. Bias-corrected and uncorrected climate simulations served as input to run the dynamic vegetation models over Wallonia. Main result of the impact of bias correction on the climate is a reduction of seasonal absolute precipitation by up to −55% with respect to uncorrected simulations. Yet, seasonal climate changes of precipitation and also temperature are marginally affected by bias correction. Main result of the impact of bias correction on changes in extremes is a robust spatial mean CCS of climate extreme indices over both domains. Yet, local biases can both over- and underestimate changes in these indices and be as large as the raw CCS. Changes in extremely wet days are locally enhanced by bias correction by more than 100%. Droughts in southern IP are exacerbated by bias correction, which increases changes in consecutive dry days by up to 14 days/year. Changes in growing season length in CE are affected by quantile mapping due to local biases ranging from 24 days/year in western CE to −24 days/year in eastern CE. The increase of tropical nights and summer days in both domains is largely affected by bias correction at the grid scale because of local biases ranging within ±14 days/year. Bias correction of this study strongly reduces the precipitation amount which has a strong impact on the results of the vegetation models with a reduced vegetation biomass and increases in net primary productivity. Nevertheless, there are large differences in the results of the two applied vegetation models.

Changes of extreme climate indices on the Mongolian Plateau during 1981-2020 based on ERA5 reanalysis

Changes of extreme climate indices on the Mongolian Plateau during 1981-2020 based on ERA5 reanalysis

Trends and impacts of climate change on crop production in Burkina Faso

Abstract Understanding past climate trends and their impacts in the Sahel region is fundamental for climate change (CC) adaptation and mitigation. This study analyses climate trends from 1961 to 2020 in three climatic zones in Burkina Faso and the impacts of CC on five major crops production. Long time series of daily rainfall and temperature data from National Meteorology Agency for the period 1961 to 2020 has been compiled. Crop production data (1984–2020) were retrieved from the agriculture department. Climate temporal variations in each climatic zone were analyzed using extreme climate indices and principal component analysis. Linear regression was used to assess climate impacts on crop production. The results showed a high rainfall variability and changes in temperature extremes in the three zones. The climate window, 1991–2020, was hotter than 1961–1990, while the last decade (2011–2020) was the wettest. Most climate indices (67%) showed significant correlations with crop yields. Dry spells, cool days, cold nights, average daily wet days and rainfall intensity showed positive and negative effects on maize, cowpea, millet and sorghum yields. This study highlights the importance of climate-smart policy promoting drought-resistant and short-duration varieties in addressing the adverse effects of CC on crop production.

Evaluating the Impact of Statistical Bias Correction on Climate Change Signal and Extreme Indices in the Jemma Sub-Basin of Blue Nile Basin

This study evaluates the effect of the statistical bias correction techniques of distribution mapping and linear scaling on climate change signals and extreme rainfall indices under different climate change scenarios in the Jemma sub-basin of the Upper Blue Nile Basin. The mean, cumulative distribution function (CDF), mean absolute error (MAE), probability of wet days (Prwet (%)), and 90th percentile (X90 (mm)) of observed rainfall and the regional climate model (RCM) simulations of rainfall with and without statistical bias correction were compared with the historical climate (1981–2005). For future (2071–2100) climate scenarios, the change in climate signal and extreme rainfall indices in the RCM simulations with and without bias correction were also evaluated using different statistical metrics. The result showed that the statistical bias correction techniques effectively adjusted the mean annual and monthly RCM simulations of rainfall to the observed rainfall. However, distribution mapping is effective and better than linear scaling for adjusting the probability of wet days and the 90th percentile of RCM simulations. In future climate scenarios, RCM simulations showed an increase in rainfall. However, the statistically bias-adjusted RCM outputs revealed a decrease in rainfall, which indicated that the statistical bias correction techniques triggered a change in climate signal. Statistical bias correction methods also result in changes in the extreme rainfall indices, such as frequency of wet days (R1mm), number of heavy precipitation days (R10mm), number of very heavy rainfall days (R20mm), and other intensity and frequency indices.

Evaluating the effects of single and integrated extreme climate events on hydrology in the Liao River Basin, China using a modified SWAT-BSR model

Extreme climate events may have a substantial impact on water conservation in the black soil region of China. This study integrated a dynamic CO2 input method and an alternative freeze–thaw cycle function into Soil and Water Assessment Tool (SWAT) to create a SWAT-Black Soil Region (SWAT-BSR) model. The SWAT-BSR model was used to simulate extreme climate events in the Liao River Basin (LRB; 220,000 km2) and an R script was developed to extract days in which extreme climate events occurred along with corresponding hydrological variables. Extreme climate events included extreme heat, cold, precipitation, and drought events across seven zones in the LRB. Subsequently, we explored the changes in single and integrated extreme climate events during the historical period (1971–2000) and future periods (2041–2070 and 2071–2100) under the SSP2-4.5 and SSP5-8.5 scenarios. The results indicated that almost each hydrological variable was reduced under extreme cold and drought events compared to the normal conditions in the historical period. Canopy interception and percolation were decreased under extreme heat in the future periods while extreme cold was opposite with a pronounced negative impact on reference evapotranspiration and soil evaporation. In contrast, each hydrological variable under extreme precipitation increased relative to the normal conditions while hydrological variables were largely negatively affected under extreme drought. In addition, the integrated extreme climate index increased over time, with more pronounced changes under the SSP5-8.5 scenario than those of SSP2-4.5 scenario. Hydrological variables were most at risk in the dryland zones as compared to partially irrigated and irrigated zones. These findings indicated that more attention should be given to the impacts of extreme climate events on water losses from both historical and future perspectives. It is also necessary to develop mitigation strategies for water conservation in the LRB based on the extreme climate events projected to occur in different zones and periods in the future.

Impact of climate change on climate extreme indices in Kaduna River basin, Nigeria.

This study examined the impact of climate change on climate extreme indices in the Kaduna River basin, Nigeria. Large-scale atmospheric variables derived from the Global Climate Model (GCM), Coupled Model Intercomparison Project Phase 5 (CMIP5) (CanESM2) were used to develop a high-resolution climate using a Statistical Down Scaling Model. The adapted Caussinus-Mestre algorithm for homogenizing networks of temperature series and multivariate bias correction based on an N-dimension probability function were used to homogenize and correct the climate data, respectively. Fifteen climate extreme indices were computed using RClimdex. The coefficient of variance, Kruskal-Wallis test, and the modified Mann-Kendall test were used to assess the variation and trends. Wavelet analysis was used to determine the periodicities of the indices (1980-2020). The findings revealed a significant warming trend with low variability of temperature indices. The moderate variability with an insignificant decreasing trend was found for rainfall indices. Similarly, the future climate indices indicate a continuing positive trend in the temperature extreme indices. The majority of climate indices have a periodicity of less than or equal to 10years for high frequency, except for PRCPTOT, R10MM, R20MM, Rx5day, SDII, TN90p, and TX90p for temperature indices. The findings conclude that the periodicity pattern of climate extreme indices is related to atmospheric phenomena (such as quasi-biennial oscillation, QBO), which indicate the impact of climate change. As a result, this can serve as an early warning for possible extreme event occurrences in the basin. The CMIP6 should be used to compare with the results of this study to provide a detailed assessment of the current implication of climate change on the catchment.

Projected Future Changes in Extreme Climate Indices over Central Asia Using RegCM4.3.5

This work projected future extreme climate indices’ changes over Central Asia (The Coordinated Regional Climate Downscaling Experiment—CORDEX Region 8). Changes were calculated for 2071–2100 relative to 1971–2000. Climate simulations were obtained by downscaling the RegCM4.3.5 to 50 km resolution under RCP4.5 and 8.5 with HadGEM2-ES and MPI-ESM-MR. The results indicate that the Central Asian domain will experience warmer and more extreme temperatures with increasing radiative forcing. The annual lowest value of minimum daily temperature was simulated to increase remarkably, up to 8 degrees, especially in high latitudes, with a more than 12 degree increase projected over Siberia. A strong growth in the percentage of warm nights and an increase in the days of warm spells for the whole region, with a decrease in cold spell duration, are anticipated. Model results show an expected reduction of up to 30% in precipitation totals over the domain, except for the increased precipitation over Siberia, the Himalayas, and Tibetan Plateau. Extreme precipitation events are projected to have an increase of 20% over the whole domain, with an 80% increase over high topographical areas.

Uncertainties on Climate Extreme Indices Estimated From U.S. Climate Reference Network (USCRN) Near‐Surface Temperatures

AbstractChanges in the frequency of temperature extremes are often attributed to global warming. The recent availability of near‐surface temperature data records from reference networks, such as the U.S. Climate Reference Network (USCRN), enables the quantification of measurement uncertainties. Within an activity of the Copernicus Climate Change Service, the estimation of the measurement uncertainty has been provided for USCRN temperature data, using metadata made available by the National Oceanic and Atmospheric Administration (NOAA). In this paper, four climate extreme indices (Frost Days, Summer Days, Ice Days, Tropical Nights) and the related uncertainties are calculated for the period 2006–2020 from the USCRN data set and compared with traditional indices. Moreover, the asymmetric USCRN measurement uncertainties are propagated to estimate the uncertainties of climate indices. The comparison shows expanded uncertainties homogeneously distributed with the latitude and typically within 15 days per year for Frost Days and within 10 days for Ice Days, while smaller uncertainties are estimated for Summer Days and Tropical Nights, with values typically within six to seven days per year. Positive uncertainties are typically larger than negative ones for all the indices. The values of Frost and Ice Days with the related uncertainties for USCRN have also been compared with the corresponding values calculated from reanalyses data, showing differences typically within 60 days for median values, quite often smaller than USCRN and inconsistent within the related uncertainties, Overall, the results show that USCRN measurement uncertainties increase confidence in the estimation of climate extreme indices and decisions for adaptation.

Response of Runoff Change to Extreme Climate Evolution in a Typical Watershed of Karst Trough Valley, SW China

Identifying the response of runoff changes to extreme climate evolution was of great scientific significance for the rational regulation of watershed water resources and the prevention of hydrological disasters. However, the time–frequency response relationships were not clear. The Yinjiang River watershed, a typical watershed with karst trough valley areas, was chosen to identify the impact of different climatic driving factors on runoff changes from 1984 to 2015. Continuous wavelet transform (CWT), cross-wavelet transform (XWT), and wavelet coherence transform (WTC) were performed to study the response relationship and time–frequency effect between runoff changes and extreme climate change at different time scales. The main results showed that: (1) Twelve extreme climate indices (ECIs) were detected to have a significant impact on runoff changes, mainly on a 6-year time scale; (2) The R10 and Rx1day in extreme precipitation index and SU34.4 and TNx in the extreme temperature index were the main driving factors of runoff changes, which had relatively large impacts on runoff changes in high and low energy vibration regions. However, the remaining eight ECIs that passed the 0.05 confidence level showed relatively large impacts on runoff changes only in low energy vibration regions; (3) The transition of the interaction between ECIs and runoff changes in high and low time–frequency scales was related to the abrupt change characteristics of the ECIs. The correlation of abrupt change was an important reason for the emergence of highly correlated regions that trigger high and low energy vibrations; (4) As a whole, the extreme precipitation events were ahead of runoff changes at the high time–frequency scale and exhibited small lag effects at the low time–frequency scale, while extreme temperature events were mainly ahead of runoff changes. This study has effectively revealed the impact of climate factors at different scales on runoff changes, and provides a theoretical understanding for regulating and managing water resources in karst basins.

Analysis of climate extremes indices in tropical South America through the RegCM4.7

AbstractIn recent decades, the frequency and intensity of extreme weather events induced by global warming have increased significantly, exerting serious impacts on the development of society and ecosystems. Based on low (RCP2.6) and high (RCP8.5) greenhouse gas emissions scenarios from the Intergovernmental Panel on Climate Change, we use the general circulation model HadGEM2‐ES that is part of the Coupled Model Intercomparison Project Phase 5 (CMIP5) to drive the regional climate model RegCM4.7. This study evaluated the ability of both models to simulate (1986–2005) and projection (2080–2099) spatio‐temporal features of extreme indices over tropical South America (TSA). Indices based on precipitation and daily maximum and minimum temperature follow the definitions of the Expert Team on Climate Change Detection and Indices (ETCCDI). In general, the RegCM4.7 reproduces accordingly the spatial distribution of the ETCCDI indices derived from precipitation and temperature on the TSA, in addition to added value to HadGEM2‐ES, especially over regions of complex topography. HadGEM2‐ES performs poorly along the west coast, due to the topography and altitude of the Andes. On the other hand, it demonstrates better performance in the Amazon Basin compared to RegCM4.7. The projection results showed that climate extreme indices based on precipitation and temperature indicate that drought events may advance to the end of the century, showing a spatial pattern that precipitation will be gradually reduced in the Amazon Basin and Northeast region of Brazil. Despite some discrepancies between the models, studies like this one help us to understand how climate change can affect regional planning and development.

Comprehensive assessment of climate extremes in high-resolution CMIP6 projections for Ethiopia

Climate extremes have more far-reaching and devastating effects than the mean climate shift, particularly on the most vulnerable societies. Ethiopia, with its low economic adaptive capacity, has been experiencing recurrent climate extremes for an extended period, leading to devastating impacts and acute food shortages affecting millions of people. In face of ongoing climate change, the frequency and intensity of climate extreme events are expected to increase further in the foreseeable future. This study provides an overview of projected changes in climate extremes indices based on downscaled high-resolution (i.e., 10 × 10 km 2) daily climate data derived from global climate models (GCMs). The magnitude and spatial patterns of trends in the projected climate extreme indices were explored under a range of emission scenarios called Shared Socioeconomic Pathways (SSPs). The performance of the GCMs to reproduce the observed climate extreme trends in the base period (1983–2012) was evaluated, the changes in the climate projections (2020–2100) were assessed and the associated uncertainties were quantified. Overall, results show largely significant and spatially consistent trends in the projected temperature-derived extreme indices with acceptable model performance in the base period. The projected changes are dominated by the uncertainties in the GCMs at the beginning of the projection period while by the end of the century proportional uncertainties arise both from the GCMs and SSPs. The results for precipitation-related extreme indices are heterogeneous in terms of spatial distribution, magnitude, and statistical significance coverage. Unlike the temperature-related indices, the uncertainty from internal climate variability constitutes a considerable proportion of the total uncertainty in the projected trends. Our work provides a comprehensive insight into the projected changes in climate extremes at relatively high spatial resolution and the related sources of projection uncertainties.

Impacts of Climate Change on Extreme Climate Indices in Türkiye Driven by High-Resolution Downscaled CMIP6 Climate Models

In this study, the latest release of all available Coupled Model Intercomparison Project Phase 6 (CMIP6) climate models with two future scenarios of Shared Socio-Economic Pathways, SSP2-4.5 and SSP5-8.5, over the period 2015–2100 are utilized in diagnosing climate extremes in Türkiye. Coarse-resolution climate models were downscaled to a 0.1° × 0.1° (~9 km) spatial resolution using the European Centre for Medium-Range Weather Forecasts Reanalysis 5-Land (ERA5-Land) dataset based on three types of quantile mapping: quantile mapping, detrended quantile mapping, and quantile delta mapping. The temporal variations of the 12 extreme precipitation indices (EPIs) and 12 extreme temperature indices (ETIs) from 2015 to 2100 consistently suggest drier conditions, in addition to more frequent and severe precipitation extremes and warming temperature extremes in Türkiye, under the two future scenarios. The SSP5-8.5 scenario indicates more severe water stress than the SSP2-4.5 scenario; the total precipitation decreases up to 20% for Aegean and Mediterranean regions of Türkiye. Precipitation extremes indicate a decrease in the frequency of heavy rains but an increase in very heavy rains and also an increasing amount of the total precipitation from very heavy rain days. Temperature extremes such as the coldest, warmest, and mean daily maximum temperature are expected to increase across all regions of Türkiye, indicating warming conditions by up to 7.5 °C by the end of the century. Additionally, the coldest daily maximums also exhibit higher variability to climate change in the subregions Aegean, Southeastern Anatolia, Marmara, and Mediterranean regions of Türkiye while the mean daily maximum temperature showed greater sensitivity in the Black Sea, Central Anatolia, and Eastern Anatolia regions.

Application of Boruta algorithms as a robust methodology for performance evaluation of CMIP6 general circulation models for hydro-climatic studies

Regional climate models are essential for climate change projections and hydrologic modelling studies, especially in watersheds that are overly sensitive to changes in climate. Accurate hydrologic model development is a daunting task in data-sparse regions where climate change’s impact on hydrologic and water quality processes is necessary for a well-informed policy decision on adaptation and hazard mitigation strategies. Novel approaches have been evolving that evaluated GCMs with the objective of improved parameterization to limit uncertainty and improve hydrologic model development. However, conclusions drawn should be purpose-driven based on intended usage. This study provides an overview of the state-of-the-art Boruta random forest as a robust methodology in the performance evaluation of GCMs models for hydroclimatic study. Highlights from the assessment indicate that (1) there is consistency in replicating the three observed climate variables of daily precipitation, maximum and minimum temperature respectively, (2) better temporal correlation (R2 = 0.95) in annual precipitation with a mean bias of 0.638mm/year, when compared to symmetrical uncertainty (SU) (R2 = 0.82), and all models ensembles (AME) (R2 = 0.88) with associated biases of 68.19mm/year and 10.57mm/year, respectively. Evaluation of the multi-year climate extreme indices, trends and magnitude reveal that there is a fair representation of basin-scale observed climate extreme events. However, the Boruta random forest approach exhibited a better statistical trend and magnitude of the extreme event in the basin. The findings of the study revealed enhanced GCM dataset evaluation and present a simple and efficient methodology to examine the limitations associated with the selected GCM ensemble for impact study in hydrology.

Trends in extreme climate indices in Cherrapunji for the period 1979 to 2020

Trends in extreme climate indices in Cherrapunji for the period 1979 to 2020

Spatial-temporal evolution and projection of climate extremes in South Korea based on multi-GCM ensemble data

Extreme climate can greatly impact social, environmental, and economic developments in human life. In this study, we evaluated the spatiotemporal trends of 21 extreme climate indices during 1975–2100 in South Korea based on observed climate data from 50 weather stations. Statistically bias-corrected daily data ensembled from 11 and 13 Global Climate Models (GCMs), archived in the Coupled Model Intercomparison Project Phases 5 and 6, respectively, were used to estimate future extreme climatology. Systematic biases in the raw GCM data were satisfactorily removed after the bias correction procedure. The extreme cold indices showed significant downward trends (p < 0.05) under both representative concentration pathway and socioeconomic concentration pathway scenarios. During the near future (2021–2060), the trends in extreme cold indices declined sharply, and during the far future (2061–2100), the trends were positive under the medium emission scenarios and moderately negative under the extreme emission scenarios. Extreme warming indices showed significant increasing trends (p < 0.05) and the change magnitude was larger than that of the extreme cold indices. Extreme warming was characterized by sharply increasing rates of severity and intensity during the near future and noticeably longer and more frequent warming events in the far future. South Korea was projected to receive substantially higher annual rainfall, particularly during the far future, featuring intense/heavy rainfall events during the monsoon season followed by dry winters. Change magnitudes of all extreme indices under RCP8.5 were much higher than those in the remaining scenarios. Compared to the rest of the country, coastal regions in the east, south, and west had more intense warming, while northern mountainous regions experienced more extreme cold. Extremely warm climatology increased steadily from coastal to inland and northern mountain regions, whereas extreme cold climatology gradually decreased from north to south. Our results showed that socially, environmentally and agriculturally important regions of South Korea were at higher risk of facing the detrimental impacts of extreme climatology.

Spatiotemporal changes of extreme climate indices and their influence and response factors in a typical cold river basin in Northeast China

Spatiotemporal changes of extreme climate indices and their influence and response factors in a typical cold river basin in Northeast China

Spatial and temporal variations of extreme climate index in the Songhua River Basin during 1961-2020.

Understanding climate change and extreme climate is of great significance for ensuring food security and socio-economic development of the Songhua River Basin. Based on the daily precipitation, maximum temperature and minimum temperature data during 1961-2020 from 69 meteorological stations in and around the Songhua River Basin, we analyzed the temporal and spatial variations of extreme temperature and precipitation in the Songhua River Basin using 27 extreme climate indices recommended by the World Meteorological Organization, and linear trend method, Mann-Kendall trend test and ordinary Kriging interpolation methodology. The results showed that, from 1961 to 2020, except for cold speel duration, the extreme cold index in the study area showed a downward trend, while the extreme warm index, extreme value index and other temperature indices showed an upward trend. The increasing trend of the minimum temperature was greater than that of the maximum temperature. Icing days, cold speel duration and warm speel duration showed an increasing trend from south to north, while the minimum value of maximum temperature and that of minimum temperature showed opposite spatial characteristics. The high value areas of summer days and tropical nights were mainly distributed in the southwestern region, while there was no obvious spatial variations of cool days, warm nights, and warm days. Overall, except for cold speel duration, other extreme cold indices had a rapid decreasing trend in the north and west of the Songhua River Basin. In the warm index, summer days, warm nights, warm days, and warm speel duration had a rapid upward trend in the north and west, and tropical nights had the fastest rise in the southwest. In the extreme value index, the maximum of temperature rose fastest in the northwest, while the minimum rose fastest in the northeast. Except for consecutive dry days, the rest of precipitation indices showed an increasing trend, and the fastest rising areas were mainly in the north-central part of the Nenjiang River Basin, while some areas in the south of the Nenjiang River Basin became dry. Heavy precipitation days, very heavy precipitation days, heaviest precipitation days, consecutive wet days, very wet day precipitation, extremely wet day precipitation, and annual precipitation showed a gradual decreasing pattern from southeast to northwest. Overall, the Songhua River Basin was warming and wetting, but there were some differences among different regions, especially the northern and southern parts of the Nenjiang River Basin.

Long-term trend analysis of extreme climate in Sarawak tropical peatland under the influence of climate change

Extreme climate is one of the important variables which determine the capability of tropical peatland to act as either carbon sink and/or carbon source. The purpose of this study is to reveal the spatio-temporal trend in the long-term time series of extreme rainfall and temperature in Sarawak peatland cause by climate change. Gridded-based Princeton datasets were used for trend analysis spanning 68-year (1948–2016) based on Modified Mann-Kendall (m-MK) test which has the capability of distinguishing unidirectional trend with multi-scale variability. The m-MK test was also used to confirm the increasing or decreasing trend produce by Mann-Kendall (MK), and to discriminate the exaggeration in trend caused by serial auto-correlation due to the high effect of large scale climate events regulating the climate in the region. By using R-based program, RClimDex for extreme climate indices output, extreme climate under Northeast (NE) and Southwest (SW) monsoon showed lower grid point with significant changes under m-MK test compared to MK test at 95% significance level. Here, the exaggeration of trend by MK test has been reduced by using m-MK test which can accommodate the scaling effect in the time series due to inherent natural climate variability. Diurnal temperature range (DTR) was expected to decrease for both monsoons in the central-coastal region as minimum temperature (TN) increased more than maximum temperature (TX). Significant increase in extreme rainfall (R10, R20, Rnn) was spatially observed more during SW monsoon compared to NE monsoon, although with high spatial variability. Significant increase of TN indices of TNn and TN90p might cause increased rainfall intensity in the south and central-coastal region, while high TX indices of TXn might cause increased rainfall intensity in the north. Due to the imminent threat of climate change, this study gives scientists an essential view on the behavior of different extreme climate variables and its potential impact on the peatland area which is susceptible to flood and risk of fire during the NE and SW monsoon, respectively.