CP-KNN: seasonal to interannual climate prediction using the KNN data mining technique. The impact of seasonal to interannual climate prediction on society, business, agriculture and almost all aspects of human life force the scientist to pay due attention to the issue. Recent years show enormous results in this field. All systems and techniques developed so far use sea surface temperature (SST) as the main factor among other seasonal climate attributes. The statistical and mathematical models are then used for further climate predictions. In this article, we will develop a system that uses historical meteorological data (rainfall, wind speed, dew point, temperature, etc.) of a region and applies the "k–nearest neighbor (KNN)" data mining algorithm for classification of this historical data. given in a specific time interval, the k nearest time intervals (K nearest neighbors) are then taken to predict the weather one month in advance. Experiments show that the system generates accurate results in a reasonable time up to a month in advance. Objectives The motivation behind the research is to extend the application of Data Mining to the fields of meteorology, oceanography and climatology. This will open a new era in the field of Data Mining and Climate Prediction. The main objectives are • Use of historical data • Data cleaning to convert data into a uniform format • Concrete model for climate prediction using data mining • Forecasting using numerical data • Improvements in performance and accuracy of the climate hypothesis climate prediction (problem solution)The enormous amount of climate data has been available for years. Data should be entered in a uniform format. If the data is noisy, it can be cleaned using any of the...... middle of paper ......on-1 and Topex/Poseidon data for seasonal climate prediction studies, AVISO Altimetry Newsletter 8, Jason- 1 Science Piano, pp. 115-116, 2002.[13] Amanda B. White. Praveen Kumar, David Tcheng; A data mining approach to understanding control over climate-induced interannual vegetation variability in the United States. Remote Sensing of Environments 98 1 – 20 (2005)[14] Jayanta Basak, Anant Sudarshan, Deepak Trivedi, MS Santhanam; Weather Data Mining Using Component Analysis, Journal of Machine Learning Research 5 239-253 2004[15] WILLIAM W. HSIEH; Nonlinear canonical correlation analysis of tropical Pacific climate variability using a neural network approach: journal of Climate Vol – 14 June 2001 Muhammad Abrar lecturer at Agricultural University Peshawar Pakistan This is the synopsis of the master's degree recently approved by the Board of Studies