Various climate extreme events in Thailand such as more recurrent and more intense floods, droughts, tropical storms, and extreme rainfall events pose increasing threats to the environment, water resources, and agricultural production. To assess the occurrence and impacts of extreme climate events, we have overviewed and investigated the changes of indices characterizing extreme precipitation provided by the Expert Team on Climate Change Detection and Indices (ETCCDI) across the country. Ten precipitation extreme indices (PRCPTOT, SDII, R10mm, R20mm, R95p, R99p, Rx1day, Rx5day, CDD, and CWD), including the strength of Asian monsoon (SWMR and NEMR) were calculated from the station data, then comparisons and projections were made with CMIP5 models. The SWMR displays high values (South and East) and low values (North, Northeast, and Central), implying high potential flood and drought hazard areas similar to the distributions of precipitation intensity extreme indices (PRCPTOT, SDII, R95p, R99p, Rx1day, and Rx5day). Most GCMs display similar spatial distribution pattern of high intensity of extreme precipitation indices to observations while there are much greater variety of results between the models and observations for the frequency indices. The multi-model ensemble (MME) projects increase in most precipitation extreme indices, indicating stronger precipitation events (by R95p and R99p) across the country notably in the west, Central, and South. Though, the MME delivers better results, some individual GCMs display high uncertainties among different RCP scenarios. Therefore, selection of suitable GCMs along with bias-correction method for regional impact study under extreme climate have to be done carefully.

泰国的各种气候极端事件，如更频繁和更强烈的洪水、干旱、热带风暴和极端降雨事件，对环境、水资源和农业生产构成越来越大的威胁。为评估极端气候事件的发生和影响，我们对全国气候变化检测与指数专家组（ETCCDI）提供的极端降水特征指标的变化进行了概述和调查。根据台站数据计算亚洲季风强度（SWMR、NEMR）等10个降水极值指数（PRCPTOT、SDII、R10mm、R20mm、R95p、R99p、Rx1day、Rx5day、CDD、CWD），并进行对比预测使用 CMIP5 模型制作。SWMR 显示高值（南部和东部）和低值（北部、东北部和中部），暗示与降水强度极端指数（PRCPTOT、SDII、R95p、R99p、Rx1day 和 Rx5day）分布相似的高潜在洪水和干旱灾害区域。大多数 GCM 显示出与观测相似的高强度极端降水指数的空间分布格局，而模式和观测的频率指数之间的结果差异更大。多模式集合 (MME) 项目在大多数降水极端指数中增加，表明全国降水事件（R95p 和 R99p）更强，特别是在西部、中部和南部。尽管 MME 提供了更好的结果，但一些单独的 GCM 在不同的 RCP 情景中显示出很高的不确定性。所以，