Daily gridded rainfall data over Peninsular Malaysia are delineated using an objective clustering algorithm, with the objective of classifying rainfall grids into groups of homogeneous regions based on the similarity of the rainfall annual cycles. It has been demonstrated that Peninsular Malaysia can be statistically delineated into eight distinct rainfall regions. This delineation is closely associated with the topographic and geographic characteristics. The variation of rainfall over the Peninsula is generally characterized by bimodal variations with two peaks, i.e., a primary peak occurring during the autumn transitional period and a secondary peak during the spring transitional period. The east coast zones, however, showed a single peak during the northeast monsoon (NEM). The influence of NEM is stronger compared to the southwest monsoon (SWM). Significantly increasing rainfall trends at 95% confidence level are not observed in all regions during the NEM, with exception of northwest zone (R1) and coastal band of west coast interior region (R3). During SWM, most areas have become drier over the last three decades. The study identifies higher variation of mean monthly rainfall over the east coast regions, but spatially, the rainfall is uniformly distributed. For the southwestern coast and west coast regions, a larger range of coefficients of variation is mostly obtained during the NEM, and to a smaller extent during the SWM. The inland region received least rainfall in February, but showed the largest spatial variation. The relationship between rainfall and the El Niño Southern Oscillation (ENSO) was examined based on the Multivariate ENSO Index (MEI). Although the concurrent relationships between rainfall in the different regions and ENSO are generally weak with negative correlations, the rainfall shows stronger positive correlation with preceding ENSO signals with a time lag of four to eight months.

Understanding the changes in temperature extremes is important for managing and coping with the risks associated with regional climate change. However, the climatological characteristics of temperature extremes and their variabilities is still not adequately studies in Malaysia. This study evaluates the spatial and temporal variations of temperature extremes over Malaysia for the period 1985‐2018. This study includes four phases: (1) collection, quality control and homogeneity analysis of temperature data; (2) general (TXmean, TNmean, TMmean and DTR), warm (TXx, TNx, TX90p and TN90p) and cool (TXn, TNn, TX10p and TN10p) temperature extreme indices calculations; (3) trend analysis of temperature extremes using the Mann‐Kendall and Sens’s slope test; and (4) analyses of correlations between temperature extremes and El Niño‐Southern Oscillation (ENSO). The results indicate a warming of surface temperature across Malaysia, particularly in Peninsular Malaysia. In general, TXmean, TNmean and TMmean increased significantly at 5% significance level by 0.12°C/decade, 0.27°C/decade and 0.17°C/decade, respectively. A significant decreasing trend in DTR by 0.12°C/decade is seen, which is related to the higher increasing rate of TNmean. All of the warm extreme indices, TXx, TNx, TX90p and TN90p presents significant increasing trends by 0.19°C/decade, 0.28°C/decade, 2.86%/decade and 3.08%/decade, respectively. For the cool extreme indices, TX10p and TN10p decreased significantly by 0.95%/decade and 3.18%/decade, respectively, though the trend in TXn by 0.11^oC/decade is not statistically significant. The only cool index presenting a significant increasing trend is TNn by 0.44°C/decade, indicating a reduction of cool nights. Besides that, major cities in the northern and western parts of Peninsular Malaysia generally exhibit a higher warming trend compared to rural and highland area. These are possibly related to the intensified urban heat island effect under the rapid urbanization. This study also found that the ENSO affected warm extreme indices much stronger than cool extreme indices in Malaysia.

This study compares five readily available gridded precipitation satellite products namely: Climate Hazards Group Infrared Precipitation with Station Data (CHIRPS) at 0.05° and 0.25° resolution, Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis (TMPA 3B42v7) and Princeton Global Forcings (PGFv3), both at 0.25°, and Global Satellite Mapping of Precipitation Reanalysis (GSMaP_RNL) at 0.1°, and evaluates their quality and reliability against 41 rain gauge stations in Malaysia. The evaluation was based on three numerical statistical scores (r, Root Mean Squared Error (RMSE) and Bias) and three categorical scores (Probability of Detection (POD), False Alarm Ratio (FAR) and Critical Success Index (CSI)) at temporal resolutions of daily, monthly and seasonal. The results showed that TMPA 3B42v7, PGFv3, CHIRPS25 and CHIRPS05 slightly overestimated the rain gauge data, while the GSMaP_RNL underestimated the value with the largest bias for monthly data. The CHIRPS25 showed the best POD score, while TMPA 3B42v7 scored highest for FAR and CSI. Overall, TMPA 3B42v7 was found to be the best-performing dataset, while PGFv3 registered the worst performance for both for numerical (monthly) and categorical (daily) scores. All products captured the intensity of heavy rainfall (20–50 mm/day) rather well, but tended to underestimate the intensity for categories of no or little rain (rain 50 mm/day). In addition, overestimation occurred for low moderate (2–5 mm/day) to low heavy rain and (10–20 mm/day). In the case study of the extreme flooding event of 2006/2007 in the southern area of Peninsular Malaysia, TMPA 3B42v7 and GSMaP_RNL performed well in capturing most heavy rainfall events but tended to overestimate light rainfalls, consistent with their performance for the occurrence intensity of rainfall at different intensity level.

This paper highlights detailed projected changes in rainfall over Thailand for the early (2011–2040), middle (2041–2070) and late (2071–2099) periods of the 21st century under the representative concentration pathways (RCP) 4.5 and RCP 8.5 using the high‐resolution multi‐model simulations of the Coordinated Regional Climate Downscaling Experiment (CORDEX) Southeast Asia. The ensemble mean is calculated based on seven members consisting of six general circulation models (GCMs) and three regional climate models (RCMs). Generally, the ensemble mean precipitation agrees reasonably well with observations, best represented by the Global Precipitation Climatology Center (GPCC) data, over Thailand during the historical period (1976–2005). However, inter‐model variations can be large among ensemble members especially during dry months (December to March) for northern‐central‐eastern parts, and throughout the year for the southern parts of Thailand. Similarly for future projection periods, inter‐model variations in the sign and magnitude of changes exist. The ensemble means of projected changes in rainfall for both RCPs during dry months show distinct contrast between the northern‐central‐eastern parts and the southern parts of Thailand with generally wetter and drier conditions, respectively. The magnitude of change can be as high as 15% of the historical period, which varies depending on the sub‐region, season, projection period, and RCP scenario. In contrast, generally drier conditions are projected during the wet season (June to September) throughout the country for both RCPs where the rainfall reduction can be as high as 10% in some areas. However, the magnitude of projected rainfall changes of some individual models can be much larger than the ensemble means, exceeding 40% in some cases. These projected changes are related to the changes in regional circulations associated with the winter and summer monsoons, which are projected to weaken. The drier (wetter) condition is associated with the enhanced subsidence (rising motion).

Palm oil production is a key industry in tropical regions, driven by the demand for affordable vegetable oil. Palm oil production has been increasing by 9% every year, mostly due to expanding biofuel markets. However, the oil palm industry has been associated with key environmental issues, such as deforestation, peatland exploitation and biomass burning that release carbon dioxide (CO₂) into the atmosphere, leading to climate change. This review therefore aims to discuss the characteristics of oil palm plantations and their impacts, especially CO₂ emissions in the Southeast Asian region. The tropical climate and soil in Southeast Asian countries, such as Malaysia and Indonesia, are very suitable for growing oil palm trees. However, due to the scarcity of available plantation areas deforestation occurs, especially in peat swamp areas. Total carbon losses from both biomass and peat due to the conversion of tropical virgin peat swamp forest into oil palm plantations are estimated to be around 427.2 ± 90.7 t C ha⁻¹ and 17.1 ± 3.6 t C ha⁻¹ year⁻¹, respectively. Even though measured CO₂ fluxes have shown that overall, oil palm plantation CO₂ emissions are about one to two times higher than other major crops, the ability of oil palms to absorb CO₂ (a net of 64 tons of CO₂ per hectare each year) and produce around 18 tons of oxygen per hectare per year is one of the main advantages of this crop. Since the oil palm industry plays a crucial role in the socio-economic development of Southeast Asian countries, sustainable and environmentally friendly practices would provide economic benefits while minimizing environmental impacts. A comprehensive review of all existing oil plantation procedures is needed to ensure that this high yielding crop has highly competitive environmental benefits.