Detection of climate zones using multifractal detrended cross-correlation analysis: A spatio-temporal data mining approach There has been a significant change in climate throughout the last few decades, resulting into the phenomenon of global warming with all its adverse effects on human life and activities. In this context, detection of climate zone is an important issue, since this may help to avert, or to take adequate measures against, any unprecedented natural calamity. Most of the existing works for this purpose are limited only to the independent study of different climate variables featuring a climate zone. In this paper, we have described a novel approach based on Multifractal Detrended Cross-correlation Analysis (MF-DXA) between each pairs of such climate variables of interest. In this approach, the spatio-temporal pattern of any location, as determined by the multifractal correlation study, has been exploited by a K-means based clustering technique, which can accurately detect various climate zones over a large region. The approach has been evaluated with the daily time series data of the year 2013 for land surface temperature and precipitation rate, collected from 73 different locations over the entire Eastern and North-Eastern region of India. The high resemblance of the identified climate zones with the World Map of Köppen-Geiger climate classification proves the accuracy and efficacy of the proposed approach.