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This study proposes a deep neural network (DNN)-based downscaling model incorporating kernel principal component analysis (KPCA) to investigate the precipitation uncertainty influenced by typhoons in Taiwan, which has a complex island topography. The best tracking data of tropical cyclones from the Joint Typhoon Warning Center (JTWC) are utilized to calculate typhoon and non-typhoon precipitation. KPCA is applied to extract nonlinear features of the BCC-CSM1-1 (Beijing Climate Center Climate System Model version 1.1) and CanESM2 (second-generation Canadian Earth System Model) GCM models. The length of the data used in the two GCM models span from January 1950 to December 2005 (historical data) and from January 2006 to December 2099 (scenario out data). The rainfall data are collected from the weather stations in Taichung and Hualien (cities of Taiwan) operated by the Central Weather Administration (CWA), Taiwan. The period of rainfall data in Taichung and in Hualien spans from January 1950 to December 2005. The proposed model is constructed with features extracted from the GCMs and historical monthly precipitation from Taichung and Hualien. The model we have built is used to estimate monthly precipitation and uncertainty in both Taichung and Hualien for future scenarios (rcp 4.5 and 8.5) of the GCMs. The results suggest that, in Taichung and Hualien, the summer precipitation is mostly within the normal range. The rainfall in the long term (January 2071 to December 2080) for both Taichung and Hualien typically fall between 100 mm and 200 mm. In the long term, the dry season (January to April, November, and December) precipitation for Taichung and that in the wet season (May to October) for Hualien are less and more affected by typhoons, respectively. The dry season precipitation is more affected by typhoons in Taichung than Hualien. In both Taichung and Hualien, the long-term probability of rainfall exceeding the historical average in the dry season is higher than that in the wet season. [ABSTRACT FROM AUTHOR]

To investigate the impacts of climate change on Taiwan, a downscaling model (DSM) was used due to the large grid size of general circulation models (GCMs). DSM is a data‐driven model based on the Radial Basis Function Neural Network (RBFNN). A Genetic Algorithm (GA) was adapted for parameter optimization, and the bootstrap method was employed to assess uncertainty. Two weather stations at similar latitudes but separated by mountains with altitudes of above 3000 m were selected as examples. Three GCMs were chosen for the model building and the assessment of near future (2050–2060) and far future (2080–2090) climate change impacts of three future scenarios A1B, A2 and B1. The results suggest that in the future, rainfall will tend to increase in winter but decrease in summer, with a similar average rainfall. In addition, our results suggest that in the future, typhoons might arrive later in the season. [ABSTRACT FROM AUTHOR]