This research investigates the spatial and temporal trend analysis of precipitation time series. Precise predictions of precipitation trends can play an imperative role in economic growth of a country. This study examined precipitation inconsistency for 23 stations at Dongting Lake, China, over a 52-year study period (1961-2012). Statistical, nonparametric Mann- Kendall (MK) and Spearman’s rho tests were applied to identify trends within monthly, seasonal, and annual precipitation. The trend-free pre-whitening method was used to exclude sequential correlation in the precipitation time series. The performance of the Mann-Kendall (MK) and Spearman’s rho tests was steady at the tested significance levels. The results showed a fusion of increasing and decreasing trends at different stations within monthly and seasonal time scales. The results obtained with the Mann-Kendall and Spearman’s rho tests showed agreement in their assessments of monthly, seasonal, and annual precipitation trends. The variability of negative and positive trends at various stations points to the need for more detailed studies on the climate change of this region. In the case of whole Dongting basin on the monthly time scale, a significant positive trend is found, while at Yuanjiang River and Xianjiag River both positive and negative significant trends are identified. Only Yuanjiang River has shown a significant trend on the seasonal time scale. No significant trends have been exhibited on the annual time scale in any case. In the case of monthly, Nanxian station exhibited the maximum positive increase in monthly precipitation during the months of July and September. In the case of seasonal, only Tongren station showed a positive trend on the monthly level, and no significant negative trends were detected in both spring and autumn seasons.