The devastating effects of drought on agriculture, water resources, and other socioeconomic activities have severe consequences on food security and water resource management. Understanding the mechanism that drives drought and predicting its variability is important for enhancing early warning and disaster risk management. In this study, meteorological droughts over six coastal synoptic stations were investigated using three-month Standardized Precipitation Index (SPI). The dry seasons of November-December-January (NDJ), December-January-February (DJF), and January-February-March (JFM) were the focal seasons for the study. Trends of dry seasons SPIs were evaluated using seasonal Mann–Kendall test. The relationship between drought SPI and ocean-atmosphere climate indices and their predictive ability were assessed using Pearson correlation and Akaike Information Criterion (AIC) stepwise regression method to select best climate indices at lagged timestep that fit the SPI. The SPI exhibited moderate to severe drought during the dry seasons. Accra exhibited a significant increasing SPI trend in JFM, NDJ, and DJF seasons. Besides, Saltpond during DJF, Tema, and Axim in NDJ season showed significant increasing trend of SPI. In recent years, SPIs in dry seasons are increasing, an indication of weak drought intensity, and the catchment areas are becoming wetter in the traditional dry seasons. Direct (inverse) relationship was established between dry seasons SPIs and Atlantic (equatorial Pacific) ocean's climate indices. The significant climate indices modulating drought SPIs at different time lags are a combination of either Nino 3.4, Nino 4, Nino 3, Nino 1 + 2, TNA, TSA, AMM, or AMO for a given station. The AIC stepwise regression model explained up to 48% of the variance in the drought SPI and indicates Nino 3.4, Nino 4, Nino 3, Nino 1 + 2, TNA, TSA, AMM, and AMO have great potential for seasonal drought prediction over Coastal Ghana.

中文翻译：

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大规模气候指数对加纳沿海气象干旱的影响

干旱对农业，水资源和其他社会经济活动的破坏性影响对粮食安全和水资源管理产生严重影响。了解导致干旱的机制并预测其变异性对于增强预警和灾害风险管理非常重要。在这项研究中，使用三个月的标准降水指数（SPI）调查了六个沿海天气站的气象干旱。研究的重点季节是11月-12月-1月（NDJ），12月-1月-2月（DJF）和1月-2月-3月（JFM）的旱季。使用季节性Mann–Kendall检验评估干旱季节SPI的趋势。使用皮尔逊相关和Akaike信息准则（AIC）逐步回归方法评估干旱SPI与海洋大气指数之间的关系及其预测能力，以在滞后时间步长中选择最适合SPI的最佳气候指数。SPI在干旱季节表现出中度至重度干旱。阿克拉在JFM，NDJ和DJF季节表现出明显的SPI趋势增长。此外，NDJ季节的DJF，Tema和Axim期间的Saltpond表现出SPI的显着增加趋势。近年来，干旱季节的SPI不断增加，这表明干旱强度较弱，而传统干旱季节的集水区却变得湿润。在干旱季节SPI和大西洋（赤道太平洋）海洋气候指数之间建立了直接（反）关系。在不同时滞下，调节干旱SPI的重要气候指数是Nino 3.4，Nino 4，Nino 3，Nino 1 + 2，TNA，TSA，AMM或AMO的组合。AIC逐步回归模型解释了干旱SPI中高达48％的方差，并表明Nino 3.4，Nino 4，Nino 3，Nino 1 + 2，TNA，TSA，AMM和AMO在沿海地区有季节性干旱预测的巨大潜力加纳。