Spatiotemporal Variability and Trends of Precipitation and Temperature in Relation to Sea Surface Temperature Teleconnections over Central Ethiopia’s Regional State
Abstract
Climate variability and climate change significantly affect poor countries whose economies depend on climate-sensitive sectors with limited adaptive capacity. This study examined the spatiotemporal variability and trends of precipitation and temperature in relation to sea surface temperature (SST) teleconnections over Central Ethiopia Regional State (CERS). Monthly precipitation data from Climate Hazards Group CHIRPS, temperature data from Climatic Research Unit CRU-TS4.08, and global SST data for 1981–2023 were analyzed using the Standardized Anomaly Index (SAI), Mann–Kendall trend test, Empirical Orthogonal Function (EOF), and Pearson correlation analysis. Results showed that Kiremt (JJAS) contributes 57.7% of annual rainfall, while Belg (FMAM) and Bega (ONDJ) contribute 32% and 10.3%, respectively. No statistically significant seasonal precipitation trends were detected at p = 0.05 although September and November rainfall exhibited significant increasing trends. EOF analysis revealed that the first three modes explained 84.23% and 92.16% of total rainfall variance during JJAS and FMAM, respectively. JJAS rainfall showed strong relationships with Pacific and Indian Ocean SSTs, whereas FMAM variability was strongly influenced by large-scale SST patterns. Temperature analysis indicated significant warming trends in minimum, mean, and maximum temperatures across all seasons. Annual temperature anomalies shifted from relatively cooler conditions in the early 1980s to persistent warming in recent decades. The study recommends season-specific agricultural planning, climate-resilient water management, improved climate information and early warning systems, and location-specific adaptation strategies.
Keywords: CERS; precipitation variability; temperature trends; sea surface temperature (SST); teleconnection; Empirical Orthogonal Function (EOF); Mann–Kendall trend test.