Abstract
Abstract. A stochastic downscaling methodology known as the Advanced Weather Generator, AWE-GEN, has been tested at four stations in Peninsular Malaysia using observations available from 1975 to 2005. The methodology involves a stochastic downscaling procedure based on a Bayesian approach. Climate statistics from a multi-model ensemble of General Circulation Model (GCM) outputs were calculated and factors of change were derived to produce the probability distribution functions (PDF). New parameters were obtained to project future climate time series. A multi-model ensemble was used in this study. The projections of extreme precipitation were based on the RCP 6.0 scenario (2081–2100). The model was able to simulate both hourly and 24-h extreme precipitation, as well as wet spell durations quite well for almost all regions. However, the performance of GCM models varies significantly in all regions showing high variability of monthly precipitation for both observed and future periods. The extreme precipitation for both hourly and 24-h seems to increase in future, while extreme of wet spells remain unchanged, up to the return periods of 10–40 years.
Highlights
Climate change is a global issue which represents an important challenge for our society as it obliges us to adapt our actions to a not well-known future climate
In this study we investigate the capability of a stochastic downscaling method, the Advanced Weather Generator (AWE-GEN), in projecting future climate variables in peninsular Malaysia
The hourly extreme precipitation is somewhat underestimated for both Station 1 and Station 3
Summary
Climate change is a global issue which represents an important challenge for our society as it obliges us to adapt our actions to a not well-known future climate. The possible changes of intensity and frequency of extreme rainfall are increasing due to the enhanced greenhouse effect (Huang et al 2011). These phenomena have attracted many meteorologists and hydrologists in the world to investigate the spatial and temporal characteristics of precipitation extremes. The General Circulation Model (GCM) can reproduce important processes about global- and continental scale atmosphere and predict future climate under different emission scenarios. There are many uncertainties in different GCMs, they are still the most adapted approach, to date, for obtaining information on climate change (Chu et al 2010)
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