Evaluation of CORDEX‐CORE regional climate models in simulating rainfall variability in Rwanda.

In this study, three regional climate models (RCMs), CCLM5‐0‐15, RegCM4‐7 and REMO2015, from CORDEX‐CORE (AFR‐22) are evaluated in their ability to reproduce rainfall variability in Rwanda for the period 1981–2005. They are driven by three different global climate models (GCMs), namely MPI‐M‐MPI‐ESM‐LR, NCC‐NorESM1‐M and MOHC‐HadGEM2‐ES, and the European Centre for Medium‐Range Weather Forecasts Reanalysis (ECMWF‐ERAINT). Simulated rainfall is evaluated against observations from Rwanda Meteorology Agency to assess models' performance. A set of metrics are used to quantify discrepancies of models' simulations from observations. A possible association of El Niño–Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) to rainfall over Rwanda is investigated. It is found that in general, all RCMs, their ensemble and multimodel ensemble means reproduce satisfactorily the spatial distribution of the mean seasonal rainfall (MSR), the mean rainfall annual cycle, and the interannual variability of the MSR for both March–April–May (MAM) and October–November–December (OND). However, significant biases in individual RCMs are observed with varying magnitude of bias in space. Observed MSR indicates a positive trend of 0.045 and 0.058 mm·day·year−1, respectively, for MAM and OND at 0.05 significance level, but almost all models indicate no significant trend (at 0.05 significance level). The seasonal correlations between observed rainfall anomalies and sea surface temperature (SST) anomalies indices across the tropical Pacific (Niño1+2 and Niño3.4) and Indian Oceans associated, respectively, with ENSO and IOD, although relatively weak, are reproduced by the three RCMs driven by ECMWF‐ERAINT and the multimodel ensemble means of ECMWF‐ERAINT and MPI‐M‐MPI‐ESM‐LR. Analysis of the Taylor diagram indicates that CCLM5‐0‐15_MPI‐M‐MPI‐ESM‐LR and the multimodel ensemble mean of MPI‐M‐MPI‐ESM‐LR outperform individual models. Overall, the evaluation finds reasonable model skill in representing seasonal rainfall climatology and variability, suggesting the potential use of CORDEX‐CORE (AFR‐22) RCMs for the assessment of future climate projections in Rwanda. [ABSTRACT FROM AUTHOR]