Modeling Spatio-Temporal Rainfall Distribution in Beni–Irumu, Democratic Republic of Congo: Insights from CHIRPS and CMIP6 under the SSP5-8.5 Scenario

Abstract

In light of the lack of ground-based observations, this study utilizes reanalysis data from the CHIRPS database and CMIP6 models under the SSP5-8.5 scenario to predict future rainfall in the Beni–Irumu region of eastern DR Congo. The use of reanalysis data offers a viable method for understanding historical and future climate trends in regions with limited ground data. Using a spatial resolution of 0.05°, selected general circulation models (GCMs) were downscaled to CHIRPS data. Analysis of historical rainfall data over 32 years reveals spatial disparities, with high-altitude regions like Mount Stanley experiencing higher annual mean rainfall (1767.87 ± 174.41 mm) compared to lower areas like Kasenyi (863.65 ± 81.85 mm), in line with orographic effects. Future projections under the SSP5-8.5 scenario indicate significant decreases in rainfall for areas such as Oicha (−565.55 mm) in the near term, while regions like Kasindi/Yihunga exhibit moderate decrease (−58.5 mm). In the mid-term, some areas show signs of recovery, with Bulongo experiencing a minor decrease (−21.67 mm), and Kasindi/Yihunga (+152.95 mm) and Kyavinyonge (+71.11 mm) showing increases. Long-term projections suggest overall improvements, with most areas experiencing positive rainfall differences; however, persistent challenges remain in Oicha (−313.82 mm). These findings highlight the dynamic impacts of climate change on rainfall distribution in the Beni–Irumu region, underscoring the need for targeted interventions to address the varied impacts, especially in vulnerable regions like Oicha.

Keywords

• climate projections
• climate modeling
• socioeconomic pathways (SSPs)
• climate resilience
• climate trends
• orographic effects