The types, application methods, and purposes of bioclimatic models are numerous, and the predictive accuracy of the models is determined by (a) the quality and quantity of data, (b) whether the user selects and uses the most appropriate model, and (c) the accuracy in forecasting climate changes. Because scientists mostly focus on (a) and (b), this paper does not discuss item (c), which requires the expertise of meteorologists. In particular, the situation described in (a) is inevitable when any model is used. However, because various models require different levels of data sensitivity, the requirements for data quality and quantity also differ. The requirements for data quality and accuracy are strict and are often based on bioclimatic models driven by data, such as the maximum entropy model, CEMs, and machine-learning models used for species distribution modeling. Thus, the preparation and compilation works of data are critical in these types of model. Two conditions are used to determine whether a user has selected and used the appropriate model. The first condition is the user’s understanding of the target organisms’ physiology, ecology, behavior, or biology. For example, if the constrain conditions of the distribution of a species is not a climatic factor, using CEMs and current species distribution data to assess the effects of climate changes on species distribution may lead to considerable errors. Therefore, to use bioclimatic models to assess the effects of climate changes on organisms, is necessary to identify the period in the target organisms’ life cycle that is most sensitive to climate changes. Subsequently, based on the period, a suitable model should be selected for conducing assessment to maximize the effectiveness of the model. Choosing an inappropriate model to conduct assessment typically results in errors (Coetzee et al. 2009).