Assessing the spatio-temporal variability and trends of climate variables and associated extreme events is fundamental for effective climate change mitigation and adaptation strategies. This study investigated the variability and trends in rainfall and nine extreme rainfall indices within the Lake Kivu climatological zone of Rwanda, utilizing data from the Rwanda Meteorology Agency for the period 1983-2021. Variability was quantified using the Coefficient of Variation, while trends and their magnitudes were determined by the Modified Mann-Kendall test and the TheilSen estimator, respectively. Results reveal a complex north-south gradient in both seasonal and annual rainfall distribution, largely influenced by topography. Seasonal rainfall ranged from 82 mm to 802 mm, while annual rainfall fluctuated between 1200 mm and 2200 mm. Regarding variability, the January-February (JF) season exhibited low variation (20-40%) in the southwestern edge but high variation (100-120%) in northcentral areas. For March-May (MAM), higher variation (80-120%) was noted in a large eastern spot between the north and central regions, while low variability (20-40%) persisted in the southwestern edge. During June-August (JJA), most areas experienced low to moderate variability (20- 80%), with a small central-north area showing higher variability (100-120%). The September-December (SOND) season was characterized by widespread moderately high variability (80-100%), with the north-central area again displaying the highest variation (100-120%). Extreme rainfall indices also demonstrated high variability, ranging from 9.43% to 182%. Overall, both total rainfall and its associated extreme indices displayed complex increasing trends, attributed to topographical variations. These findings provide critical evidence to guide further research, inform policy, and support decision-making in developing effective climate change mitigation and adaptation strategies within the Lake Kivu climatological zone. Keywords : Extreme indices, Lake Kivu climate zone, Rainfall Variability, Trends, spatio-temporal analysis
Mbonigaba et al. (Wed,) studied this question.