Journal of Intelligent Systems and Internet of Things JISIoT 2690-6791 2769-786X 10.54216/JISIoT https://www.americaspg.com/journals/show/2115 2019 2019 Department of System Programming, South Ural State University, 454080 Chelyabinsk, Russia Hussein Alkattan Department of System Programming, South Ural State University, 454080 Chelyabinsk, Russia Sanjar Abdullaev Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura, 35111, Egypt El-Sayed M. El El-Kenawy The "climate in the weather" (CW) approach, which combines the scientific and everyday sense of climate, has been proposed. The CW is based on the in-depth idea of E. E. Fedorov to classify regional climates as an ensemble of daily weather states. We have transformed this idea into a nonparametric method of processing meteorological series, where each member of the series is mapped to quantiles of corresponding distributions, and then new time series are formed, where meteorological variables are replaced by their quantiles. Next, the members of the new quantized series are combined in weather states. In this work, by using quantiles combination of monthly temperature and precipitation, we construct four CW states - "cold and dry", "cold and rainy", "warm and rainy", "warm and dry". Then we demonstrate the possibility of the CW approach to analyze space-time climate similarity and climate change in the Mesopotamian River system. The application of 16 CW states is also discussed. The climate change dynamical assessment (CCDA) showed that the Euphrates (Tigris) tributaries values varied from 13 to 19% (13-25%) with a clear increase in Greater Zab, Lesser Zab, Adhaim, and Dyala basins. The analysis of CW-altered states demonstrated that climate change is occurred due to an increase in temperature, a decrease in precipitation, and mixed changes simultaneously. In each of the basins, there were a different number of such changes. The "climate in weather" approach developed can be used for processing multidimensional meteorological time series data and outlining the general conception of the regional climate. 2023 2023 48 65 10.54216/JISIoT.100104 https://www.americaspg.com/articleinfo/18/show/2115