Accurate and timely rainfall prediction enhances productivity and can aid proper planning in sectors such as agriculture, health, transport and water resources. However quantitative rainfall prediction is normally a challenge and for this reason, this study was conducted with an aim of improving rainfall prediction using ensemble methods. It first assessed the performance of six convective schemes (Kain–Fritsch (KF); Betts–Miller–Janjić (BMJ); Grell–Fretas (GF); Grell 3D ensemble (G3); New–Tiedke (NT) and Grell–Devenyi (GD)) using the root mean square error (RMSE) and mean error (ME) focusing on the March–May 2013 rainfall period over Uganda. 18 ensemble members were then generated from the three best performing convective schemes (i.e., KF, GF and G3). The daily rainfall predicted by the three ensemble methods (i.e., ensemble mean (ENS); ensemble mean analogue (EMA) and multi–member analogue ensemble (MAEM)) was then compared with the observed daily rainfall and the RMSE and ME computed. The results shows that the ENS presented a smaller RMSE compared to individual schemes (ENS: 10.02; KF: 23.96; BMJ: 26.04; GF: 25.85; G3: 24.07; NT: 29.13 and GD: 26.27) and a better bias (ENS: −1.28; KF: −1.62; BMJ: −4.04; GF: −3.90; G3: −3.62; NT: −5.41 and GD: −4.07). The EMA and MAEM presented 13 out of 21 stations and 17 out of 21 stations respectively with smaller RMSE compared to ENS thus demonstrating additional improvement in predictive performance. This study proposed and described MAEM and found it producing comparatively better quantitative rainfall prediction performance compared to the other ensemble methods used. The MAEM method should be valid regardless the nature of the rainfall season.

Understanding variations in rainfall in tropical regions is important due to its impacts on water resources, health and agriculture. This study assessed the dekadal rainfall patterns and rain days to determine intra-seasonal rainfall variability during the March–May season using the Mann–Kendall ( $MK$ ) trend test and simple linear regression ( $SLR$ ) over the period 2000–2015. Results showed an increasing trend of both dekadal rainfall amount and rain days (third and seventh dekads). The light rain days ( $SLR$ = 0.181; $MK$ = 0.350) and wet days ( $SLR$ = 0.092; $MK$ = 0.118) also depict an increasing trend. The rate of increase of light rain days and wet days during the third dekad (light rain days: $SLR$ = 0.020; $MK$ = 0.279 and wet days: $SLR$ = 0.146; $MK$ = 0.376) was slightly greater than during the seventh dekad (light rain days: $SLR$ = 0.014; $MK$ = 0.018 and wet days: $SLR$ = 0.061; $MK$ = 0.315) dekad. Seventy-four percent accounted for 2–4 consecutive dry days, but no significant trend was detected. The extreme rainfall was increasing over the third ( $MK$ = 0.363) and seventh ( $MK$ = 0.429) dekads. The rainfall amount and rain days were highly correlated (r: 0.43–0.72).

Accurate and timely rainfall prediction enhances productivity and can aid proper planning in sectors such as agriculture, health, transport and water resources. This study is aimed at improving rainfall prediction using ensemble methods. It first assesses the performance of six convective schemes (Kain–Fritsch (KF); Betts–Miller–Janji´c (BMJ); Grell–Fretas (GF); Grell 3D ensemble (G3); New–Tiedke (NT) and Grell–Devenyi (GD)) using the root mean square error (RMSE) and mean error (ME) focusing on the March–May 2013 rainfall period over Uganda. 18 ensemble members are generated from the three best performing convective schemes (i.e. KF, GF & G3). The performance of three ensemble methods (i.e. ensemble mean (EM); ensemble mean analogue (EMA) and multi–member analogue ensemble (MAEM)) is also analyzed using the RMSE and ME. The EM presented a smaller RMSE compared to individual schemes (EM:10.02; KF:23.96; BMJ:26.04; GF:25.85; G3:24.07; NT:29.13 & GD:26.27) and a better bias (EM:-1.28; KF:-1.62; BMJ:-4.04; GF:-3.90; G3:-3.62; NT:-5.41 & GD:-4.07). The EMA and MAEM presented 13 out of 21 stations & 17 out of 21 stations respectively with smaller RMSE compared to EM thus demonstrating additional improvement in predictive performance. The MAEM is a new approach proposed and described in the study.