Convection-permitting climate simulations using SINGV-RCM and WRF over the Maritime Continent

This study evaluates two regional climate models—SINGV-RCM and WRF—forced by ERA5 reanalysis data and configured with the same spatial resolution and domain sizes over the period 2001–2014. The focus is on assessing each model’s ability to simulate observed precipitation characteristics over the Maritime Continent (MC) in general, and the Western Maritime Continent (WMC) in particular, including convection-permitting (CP) configurations at 2 km and 8 km resolutions. Both models capture the overall envelope of the extremes and the diurnal cycle of precipitation over the MC domain. While SINGV-RCM tends to overestimate higher percentiles of rainfall over land for extreme precipitation events, it simultaneously exhibits a dry bias in oceanic regions. Although WRF simulates lower extreme rainfall values than SINGV-RCM when evaluated against IMERG satellite-derived data over land grid points, both models adequately reproduce the summer (JJAS) diurnal cycle and the progression of peak rainfall over the WMC. However, notable discrepancies remain during the winter months (NDJF), especially over oceanic regions. These inter-model discrepancies stem from variations in their underlying physical and dynamical schemes, despite using identical domain sizes, spatial resolutions, and forcing fields. Observational uncertainty still exist in this study due to the use of IMERG alone. Additionally, the results underscore the added value of using convection-permitting resolutions—even at 8 km—in improving precipitation simulations in this complex region.

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Publication Details

Journal
Climate Dynamics
Published
2026-05-01
DOI
https://doi.org/10.1007/s00382-026-08193-1
Primary Topic
Climate variability and models
Type
article
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article

Convection-permitting climate simulations using SINGV-RCM and WRF over the Maritime Continent

Pavan Harika Raavi, Gerald Lim, Venkatraman Prasanna, Srivatsan V. Raghavan et al.
Climate Dynamics
Climate variability and models
article

Convection-permitting climate simulations using SINGV-RCM and WRF over the Maritime Continent

Pavan Harika Raavi, Gerald Lim, Venkatraman Prasanna, Srivatsan V. Raghavan, Fei Luo, Anurag Dipankar, Stuart Webster, Xin Rong Chua, Chen Chen, Jianyu Liu, Nguyễn Ngọc Sơn, Aurel Moise, Claudio Sanchez, Bhenjamin Jordan Lozada Ona, Anupam Kumar, Jianjun Yu, Hassim Eeqmal Muhammad, Sahany Sandeep
article en

Abstract

This study evaluates two regional climate models—SINGV-RCM and WRF—forced by ERA5 reanalysis data and configured with the same spatial resolution and domain sizes over the period 2001–2014. The focus is on assessing each model’s ability to simulate observed precipitation characteristics over the Maritime Continent (MC) in general, and the Western Maritime Continent (WMC) in particular, including convection-permitting (CP) configurations at 2 km and 8 km resolutions. Both models capture the overall envelope of the extremes and the diurnal cycle of precipitation over the MC domain. While SINGV-RCM tends to overestimate higher percentiles of rainfall over land for extreme precipitation events, it simultaneously exhibits a dry bias in oceanic regions. Although WRF simulates lower extreme rainfall values than SINGV-RCM when evaluated against IMERG satellite-derived data over land grid points, both models adequately reproduce the summer (JJAS) diurnal cycle and the progression of peak rainfall over the WMC. However, notable discrepancies remain during the winter months (NDJF), especially over oceanic regions. These inter-model discrepancies stem from variations in their underlying physical and dynamical schemes, despite using identical domain sizes, spatial resolutions, and forcing fields. Observational uncertainty still exist in this study due to the use of IMERG alone. Additionally, the results underscore the added value of using convection-permitting resolutions—even at 8 km—in improving precipitation simulations in this complex region.

Climate DynamicsVol. 64(5)
Met Office (GB), National University of Singapore (SG), ETH Zurich (CH), Clinical Nutrition Research Centre (SG)
Climate action
Openalex Percentile: Top 5%
Climate variability and models
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