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The rise of global surface temperature due to warming climate is expected to increase the intensity and occurrence of extreme precipitation events. Previous studies in Southeast Asia revealed complex variations in changes of precipitation extremes. This study presents a spatial–temporal analysis on changes of precipitation extremes in Peninsular Malaysia by utilizing long‐term daily rainfall records at 64 observed stations during 1989–2018. The modified Mann–Kendall and Sen's slope tests were performed to detect the significance and magnitude of trends in eight extreme precipitation indices recommended by the Expert Team on Climate Change Detection and Indices. Statistically significant increasing trends are observed for four of these extreme indices in the annual assessment. Spatial analysis demonstrates an obvious contrast between wet and dry regions in patterns of precipitation extremes. Seasonal analysis reveals the intensity and frequency of wet extremes are enhanced significantly during the northeast monsoon season. Significant correlations are found between precipitation extremes and El Niño–Southern Oscillation, particularly in the northern, eastern and southwest regions. Collectively, the evidence presented suggests that the occurrence of precipitation extremes in Peninsular Malaysia tends to be more frequent and intense over the year and is closely associated with the combined effects of tropical monsoon cycles and El Niño–Southern Oscillation. [ABSTRACT FROM AUTHOR]

This study investigates the capability of both quantile mapping (QM) bias correction and kriging merging techniques to improve precipitation accuracy of Tropical Rainfall Measuring Mission (TRMM) and Integrated Multisatellite Retrievals for the Global Precipitation Measurement (IMERG) satellite estimations over the Langat River Basin, an important river basin in Malaysia as it is the main source of potable water supply to Kuala Lumpur, in the 5-year period (2014–2018). This analysis also integrates both techniques to investigate whether the estimations can be further improved. Findings show that the estimations that undergo QM first followed by kriging merging (QK-TRMM and QK-IMERG) give significant improvement at almost all aspects of rainfall and streamflow comparison. At point-to-pixel rainfall comparison, around 50% improvement can be seen in both time series– and frequency-based statistics as well as an able to perform with a coefficient of correlation (CC) over 0.80 in terms of areal rainfall. The study performs streamflow simulation by employing the hydrological modeling system (HEC-HMS) to validate the performance of raw and enhanced satellite estimations for the 2014–2015 extreme flood events. Both QK-TRMM and QK-IMERG show a great improvement in the overall streamflow simulation with a Nash–Sutcliffe efficiency (NSE) of more than 0.70. The results reveal that the newly proposed bias correction method (merging of the QM and kriging methods) has significantly contributed to the improvement of precipitation estimation, which is crucial in water resources planning and flood forecasting. [ABSTRACT FROM AUTHOR]