Projecting the mortality of cardiovascular disease in future is crucial in preparing the mitigation strategies. The purpose of this research is to estimate number of deaths of the cardiovascular disease in Peninsular Malaysia based on future temperature projections using the cluster approach. Ward's method is used to identify the number of clusters of 45 meteorological stations by calculating the shortest distance between the two coordinates of the stations. The output of global climate model (GCM) is incredibly useful for the projection of future temperature, but the large bias in the observational datasets may lead to inaccurate projection. To tackle the bias, a good fitted model for temperature series is important in order to ensure that the mean and variability of the observed series are well captured. It is important to estimate the parameters for each cluster precisely. Furthermore, a good fitted model for temperature series is also crucial in order to ensure that the mean and variability of the observations are well captured. Thus, this study proposed the appropriate statistical distribution for the temperature series to be associated in the bias correction method (BCM) using the quantile mapping (QM) technique to reduce the biases between observations and historical GCM temperature data series. Next, Ward's method is applied to determine the optimal number of clusters for Peninsular Malaysia. The results have shown that the proposed model is able to reduce the temperature series biases between the GCM and the observations. Six clusters throughout Peninsular Malaysia have been selected based on Ward's method. The projection number of deaths of cardiovascular disease under is estimated to increase between 2006 and 2100 in all clusters across Peninsular Malaysia, based on the temperature projections.

Bias correction method (BCM) is useful in reducing the statistically downscaled biases of global climate models’ (GCM) outputs and preserving statistical moments of the hydrological series. However, BCM is less efficient under changed future conditions due to the stationary assumption and performs poorly in removing bias at extremes, thereby producing unreliable bias-corrected data. Thus, the existing BCM with normal distribution is improved by incorporating skewed distributions into the model with linear covariate (BCM-QMskewed). In this study, BCM-QMskewed is developed to reduce biases in the extreme temperature data of peninsular Malaysia. The input is the MIROC5 model output gridded data and observations sourced by the Malaysian Department of Irrigation and Drainage (1976–2005). BCM-QMskewed with lognormal (LGNORM) and Gumbel (GUM) has shown considerable skills in correcting biases, capturing extreme and nonstationarity of current and future extreme temperatures data series corresponding to the representative concentration pathways (RCPs) for 2006–2100 based on model diagnostics and precision analysis. Higher projection of extreme temperatures is more pronounced under RCP8.5 than RCP4.5 with precise estimates ranging from 33 to 42 °C and 30 to 32 °C, respectively. Finally, the projection of extreme temperatures is used to calculate cardiovascular disease (CVD) mortality rate which coincides with high extreme temperatures ranging between 0.002 and 0.014.