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Rising temperatures due to global warming necessitate immediate evaluation of heatwave patterns in Peninsular Malaysia (PM). For this purpose, this study utilized a locally developed heatwave index and a gridded daily maximum temperature (Tmax) dataset from ERA5 (1950–2022). During validation, the ERA5 dataset accurately represented the spatial pattern of Level 1 heatwaves, showing widespread occurrence. Historically, Level 1 heatwaves prevailed at 63.0%, followed by Level 2 at 27.7%, concentrated in northwestern states and the enclave between the Tahan and Titiwangsa mountain ranges. During very strong El Niño events in 1982/83, 1997/98, and 2015/16, Level 2 heatwave distributions were 10.4%, 26.8%, and 15.0%, respectively. For future projection, the model ensemble was created by selecting top-performing Global Climate Models (GCMs) using Kling-Gupta efficiency (KGE), ranked re-aggregation with compromise programming index (CPI), and GCM subset selection via Fisher-Jenks. The linear scaling bias-corrected GCMs (BC-GCMs), NorESM2-LM, ACCESS-CM2, MPI-ESM1-2-LR, ACCESS-ESM1-5, and FGOALS-g3, were found to exhibit better performance, and then ensemble. March to May show the highest increase in all scenarios, ranging from 3.3 °C to 4.4 °C for Level 1 heatwaves and 4.1 °C to 10.7 °C for Level 2 heatwaves. In the near future, SSP5-8.5 projects up to a 40.5% spatial increase for Level 1 heatwaves and a 2.3% increase for Level 2 heatwaves, affecting 97.1% and 57.2% of the area, respectively. In the far future, under SSP2-4.5 and SSP5-8.5, Tmax is projected to rise rapidly (1.5–4.5 °C) in the northern, western, and central regions, with increasing population exposure anticipated in the northern and western regions. [ABSTRACT FROM AUTHOR]

Climate change has significantly increased human thermal stress, particularly in tropical regions, exacerbating associated risks and consequences, such as heat-related illnesses, decreased workability, and economic losses. Understanding the changes in human thermal stress and its drivers is crucial to identify adaptation measures. This study aims to assess various meteorological variables' spatial and seasonal impact on Wet Bulb Globe Temperature (WBGT), an indicator of human thermal stress, in Peninsular Malaysia. The Liljegren method is used to estimate WBGT using ERA5 hourly data from 1959 to the present. The trends in WBGT and its influencing factors are evaluated using a modified Mann-Kendall test to determine the region's primary driver of WBGT change. The results indicate that air temperature influences WBGT the most, accounting for nearly 60% of the variation. Solar radiation contributes between 20% and 30% in different seasons. Relative humidity, zenith, and wind speed have relatively lesser impacts, ranging from −5% to 20%. Air temperature has the highest influence in the northern areas (>60%) and the lowest in the coastal regions (40%). On the other hand, solar radiation has the highest influence in the southern areas (20–40%) and the least in the north. The study also reveals a significant annual increase in temperature across all seasons, ranging from 0.06 to 0.24 °C. This rapid temperature rise in the study area region has led to a substantial increase in WBGT. The higher increase in WBGT occurred in the coastal regions, particularly densely populated western coastal regions, indicating potential implications for public health. These findings provide valuable insights into the factors driving WBGT and emphasize the importance of considering air temperature as a key variable when assessing heat stress. [ABSTRACT FROM AUTHOR]

Climate change has significantly altered the characteristics of climate zones, posing considerable challenges to ecosystems and biodiversity, particularly in Borneo, known for its high species density per unit area. This study aimed to classify the region into homogeneous climate groups based on long-term average behavior. The most effective parameters from the high-resolution daily gridded Princeton climate datasets spanning 65 years (1950–2014) were utilized, including rainfall, relative humidity (RH), temperatures (Tavg, Tmin, Tmax, and diurnal temperature range (DTR)), along with elevation data at 0.25° resolution. The FCM clustering method outperformed K-Mean and two Ward's hierarchical methods (WardD and WardD2) in classifying Borneo's climate zones based on multi-criteria assessment, exhibiting the lowest average distance (2.172–2.180) and the highest compromise programming index (CPI)-based correlation ranking among cluster averages across all climate parameters. Borneo's climate zones were categorized into four: 'Wet and cold' (WC) and 'Wet' (W) representing wetter zones, and 'Wet and hot' (WH) and 'Dry and hot' (DH) representing hotter zones, each with clearly defined boundaries. For future projection, EC-Earth3-Veg ranked first for all climate parameters across 961 grid points, emerging as the top-performing model. The linear scaling (LS) bias-corrected EC-Earth3-Veg model, as shown in the Taylor diagram, closely replicated the observed datasets, facilitating future climate zone reclassification. Improved performance across parameters was evident based on MAE (35.8–94.6%), MSE (57.0–99.5%), NRMSE (42.7–92.1%), PBIAS (100–108%), MD (23.0–85.3%), KGE (21.1–78.1%), and VE (5.1–9.1%), with closer replication of empirical probability distribution function (PDF) curves during the validation period. In the future, Borneo's climate zones will shift notably, with WC elongating southward along the mountainous spine, W forming an enclave over the north-central mountains, WH shifting northward and shrinking inland, and DH expanding northward along the western coast. Under SSP5-8.5, WC is expected to expand by 39% and 11% for the mid- and far-future periods, respectively, while W is set to shrink by 46%. WH is projected to expand by 2% and 8% for the mid- and far-future periods, respectively. Conversely, DH is expected to expand by 43% for the far-future period but shrink by 42% for the mid-future period. This study fills a gap by redefining Borneo's climate zones based on an increased number of effective parameters and projecting future shifts, utilizing advanced clustering methods (FCM) under CMIP6 scenarios. Importantly, it contributes by ranking GCMs using RIMs and CPI across multiple climate parameters, addressing a previous gap in GCM assessment. The study's findings can facilitate cross-border collaboration by providing a shared understanding of climate dynamics and informing joint environmental management and disaster response efforts. • Borneo was classified into several climate zones based on cluster analysis. • Fuzzy C-Mean clustering method can better classify the climate zone of Borneo. • Selection of CMIP6 models was done by employing relative importance metrics. • EC-Earth3-Veg was selected for future climate zones under SSP2-4.5 and SSP5-8.5. • The hotter climate zones, namely, WH and DH, are expected to expand in the future. [ABSTRACT FROM AUTHOR]

Although Borneo Island is one of the most vulnerable tropical regions to climate change, maps depicting the local climate conditions employing climate classification are still not well defined. The present study attempted regional climate classification to divide the Borneo region into several homogenous groups based on long-term average climate behavior. Daily gridded rainfall and temperature (Tavg, Tmax, and Tmin) data at 0.25° resolution spanning 56 years (1960–2016) was used. The classification was done using non-hierarchical k-mean and several hierarchical methods, namely, Single, Complete, McQuitty, Average, Centroid, Median, and two algorithms of Ward's method, wardD, and wardD2. The results showed that k-mean, wardD, and wardD2 were able to classify the climate of Borneo into four zones, namely 'dry and hot' (DH), 'wet and hot' (WH), 'wet' (W), and 'wet and cold' (WC) with a considerable difference at the boundaries. Spatial relevancy, stability, and variability of the clusters based on correlation and compromise programming showed that the wardD method was the most likely to yield acceptable results with optimum 4-cluster to partition the area into four principal climate zones. The constructed cluster plot, centroid plot, and probability distribution function (PDF) showed a distinct climatic characteristic between the climate zones in terms of rainfall, temperature, and seasonality. The proposed climate zonation for Borneo can help in better understanding climate regionality and climate-related development planning. [ABSTRACT FROM AUTHOR]

A study has been conducted to evaluate the changes in temperature of Borneo Island for different representative concentration pathway (RCP) scenarios through statistical downscaling of an ensemble of general circulation models (GCMs) selected using entropy-based methods. A combination of past-performance and envelope approaches was used for the selection of GCM ensemble from a pool of 20 Coupled Model Intercomparison Project Phase 5 (CMIP5) GCMs. Ranking of the GCMs was done separately at each grid point by using three entropy-based feature selection methods. Climate Research Unit (CRU) gridded temperature data was used as dependent variable and the simulated temperature of different GCMs for the period 1961–2005 was used as independent variable for the selection of GCMs. The scores obtained by the GCMs at different CRU grids were aggregated using compromise programming method and then ranked for entire Borneo Island using group decision-making method. Linear scaling method was applied to remove the biases in GCMs, followed by random forest (RF)–based regression method to generate multi-model ensemble projections. The results revealed that FIO-ESM, MRI-CGCM3, GFDL-CM3, and IPSL-CM5A-MR are the most suitable GCMs for the projection of temperature in the study area. The projection of temperature using the selected GCMs indicated increase in temperature in the Borneo Island, particularly in the southwest regions. The minimum temperature was projected to increase more (3.3 to 4.7 °C) compared with maximum temperature (3.0 to 4.6 °C) and thus, the diurnal temperature range (DTR) was projected to decrease gradually until the end of the twenty-first century. Increase in average and maximum temperatures was projected more during the southwest (SW) monsoon compared with the northeast (NE) monsoon. The study indicated that large increase in temperature and decrease in DTR would have significant impact on ecology and bio-environment of the island which have one of the dense ecological diversities in the world. [ABSTRACT FROM AUTHOR]