The quantitative study on the impact of climate change on tourism in China is relatively weak, so it is urgent to learn from foreign experience. Therefore, based on the SCI/SSCI literature database, this article reviews the related research progress abroad from 1986 to 2017. The study finds that for more than 30 years, quantitative research on the impact of climate change on tourism in foreign countries has mainly used index methods, tourism demand models and selection analysis methods. Among them, the index method includes the single index method and the comprehensive index method. The tourism demand model includes the time series model and the cumulative demand model. The selection analysis includes the descriptive statistics and the discrete selection model. The indicator method is mainly used to study the environmental effects of tourism resources and environmental changes, changes in tourism climate conditions, changes in comprehensive factors, and the climate change response behavior of the main body of tourism. Due to the existence of offsetting effects of climate change, the comprehensive index method is more advantageous than the single index method. Although the comprehensive index method has difficulties such as computational complexity, it can comprehensively examine the impact of climate change on the comprehensive factors of tourism destinations, and is an important direction of development of indicators and methods. The indicator approach focuses on the changes in tourism destinations, and climate change responses need to understand the changes in tourism demand. Therefore, the use of tourism demand model has gradually increased. Among them, the time series method is mainly used to study the impact of weather conditions on tourism demand. The cumulative demand model is mainly used to study the structural impact of climate change on tourism demand and the impact of climate policy on tourism demand. With the development of computer technology and artificial intelligence, there is a great potential for future applications. The tourism demand model focuses on changes in the macro-tourism flow and ignores the heterogeneity of the tourism market. With the diversification and diversity of the tourism market becoming more apparent, the use of micro-individual-based selection analysis methods has increased. In related studies of selective analysis, descriptive statistics are often used to study the effects of climate change based on preference, behavioral willingness and climate change perception in the context of climate change. Discrete choice models are often used to study the influence of climate change based on preference and help to analyze the changes in the market structure of tourist destinations in the context of climate change. As more and more studies show that the impact of climate change on the tourism market is more reflected in the change in market structure, the application demand for discrete selection models has further increased. However, the basic theoretical assumptions of the discrete selection model still need to be studied in the correction of tourism scenarios. Combining the latest progress in the quantitative research on the impact of climate change on tourism in foreign countries, and linking with China's reality, future research needs to strengthen the application of cumulative demand models in tourism flow related research, the application of discrete selection models in tourism market structure research, and the use of systematic scientific methods and big data technologies in related research. In the future, we should enhance research on climate-sensitive tourism activities in China, and as relevant studies on "Belt and Road" countries and regions, as well as the Tibetan Plateau.