Volume 10 Article informations Identification of relationships between climate indices and precipitation fluctuation in Peshawar City-Pakistan Bushra Begum a , Sapna Tajbar b, , Banaras Khan a , Lubna Rafiq c Abstract The demand for water has increased in the recent decades in all sectors that are domestic, industrial, and agriculture. Climate signals teleconnection are one of the important factors influencing the oscillations of climate on earth (Ruigar and Golian, 2015). The aim of the present study was to identify the relationship between climate indices such as El Nino Southern Oscillation (Nino-1+2, Nino-3, Nino-3.4, Nino-4, Southern Oscillation Index, Multivariate ENSO Index and Sea Surface Temperature), Quasi-Biennial Oscillation and the precipitation in Peshawar CityPakistan and predict the precipitation. The Tropical Rainfall Measuring Mission satellite precipitation and National Oceanic and Atmospheric Administration climate indices data for the period of 1982 to 2018 were used and Pearson correlations, cross-correlations and random forest model were applied. The results manifested that on monthly basis, the highest significant correlations were observed for SOI during June (0.48), Nino-1+2 during May (0.47), Sea surface temperature during June (0.45), August (0.42), and October (0.46), Nino-3 and Nino-3.4 in November (0.44 and 0.43, respectively), and Nino-4 during December (-0.44). Seasonal analysis showed positive significant correlations for Southern Oscillation Index (0.44), Quasi-Biennial Oscillation (0.34), Multivariate ENSO Index (0.39) and Nino-3 (0.34) indices during summer, autumn, spring, and autumn seasons, respectively. On annual basis, no significant correlations were noticed. In the antecedent correlation analysis, six climate indices have the maximum lagged correlations. During prediction, the model performed well in training period and the predicted precipitation followed the trend, but its performance was low on the extreme precipitation phases and similarly during the test period. The findings of this study are of importance to help policy makers in decisions making and planning for adaptation to the effects of climate change. Key words: Precipitation, Climate indices, Pearson correlation analysis, Cross-correlation, Random Forest, Peshawar City-Pakistan.