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Analyzing future rainfall variations over southern malay peninsula based on CORDEX-SEA dataset

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Abstract 

Gridded rainfall datasets based on various data sources and techniques have emerged to help describe the spatiotemporal features of rainfall patterns over large areas and have gained popularity in many regional/global climatic analyses. This study explored future variations of rainfall characteristics over peninsula Malaysia and Singapore region based on rainfall indices of PRCPTOT, Rx1day, Rx5day, R95pTOT, R1mm, and R20mm, under 9 CORDEX-SEA RCM datasets with RCP8.5 emission scenario. A monthly quantile delta mapping method (MQDM) was adopted for bias-correction of the RCM modelled data. It was indicated that all the studied rainfall indices have long-term variations both temporally and spatially. Generally, the further the future, the higher the variability and uncertainty of indices. For the study region, the relative increments of the medians from RCM models averaged over all climatic zones in the far future are 40.3%, 25.9%, and 4.7% for Rx1day, Rx5day and R95pTOT, respectively. The annual rainfall amount (PRCPTOT) in the long run would likely increase mainly in the northeast coastal zone and drop in most of other areas over the peninsula, with the median being -5.9% averaged over all zones. The frequency of wet days (R1mm) would generally drop over the whole peninsula, with the median averaged over all zones being -6.8% in the far future. The frequency of heavy rains (R20mm) would overall decrease (by -3.4% in average in the far future) but might still notably increase in the northeast zone (NE) at both annual and southwest monsoon. The extreme conditions implied from individual RCM models could be more alarming. The study result could be useful in revealing the essential spatiotemporal variations of rainfall over the peninsula from short- to long-term futures and supporting large-scale flood risk assessment and adaptation planning.

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Data availability

The data used to support the findings of this study are available from the corresponding author upon request.

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Acknowledgements

The authors would like to acknowledge the financial support from the Singapore MOE Academic Research Fund Tier 1 (Grant No. RG85/19) and in part (Grant No. RG72/22) for the completion of this research. We acknowledge the data support from APHRODITE (http://aphrodite.st.hirosaki-u.ac.jp/) and the Southeast Asia Regional Climate Downscaling (SEACLID)/Coordinated Regional Climate Downscaling Experiment for Southeast Asia (CORDEX-SEA) project (which was funded by ARCP2014-07CMY-Tangang and ARCP2015-04CMY-Tangang). We thank the World Climate Research Programme's Working Groups on both Regional Climate and Coupled Modelling, as well as other climate modelling groups for producing and making available their model output. We also acknowledge the Earth System Grid Federation infrastructure an international effort led by the U.S. Department of Energy's Program for Climate Model Diagnosis and Intercomparison, the European Network for Earth System Modelling and other partners in the Global Organisation for Earth System Science Portals (GO-ESSP). We deeply appreciate the comments from the reviewers who have helped improve the manuscript significantly.

Funding

This study was supported by AcRF Tier 1 project (RG85/19) and in part by AcRF Tier 1 project (RG72/22) from Ministry of Education (MOE), Singapore.

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Authors and Affiliations

  1. School of Civil and Environmental Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore

    Xiaosheng Qin & Lilingjun Liu

  2. School of Civil Engineering, Sun Yat-Sen University, Guangzhou, 510275, China

    Chao Dai

Authors
  1. Xiaosheng Qin
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  2. Chao Dai
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Contributions

Dr. Xiaosheng Qin: writing—original draft, conceptualization, methodology, formal analysis, data curation, visualization; Dr. Dai Chao: review & editing, validation; Mr. Liu Lilingjun: review & editing, data curation.

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Correspondence to Xiaosheng Qin.

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The code used to support the findings of this study are available from the corresponding author upon request.

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Qin, X., Dai, C. & Liu, L. Analyzing future rainfall variations over southern malay peninsula based on CORDEX-SEA dataset. Theor Appl Climatol 152, 407–419 (2023). https://doi.org/10.1007/s00704-023-04422-3

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