Abstract
The present study proposes a Distributional Added Value (DAV) analysis of CORDEX-CORE RegCM4-7 precipitation simulations over Africa, with the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR) data as reference, which is chosen based on a preliminary assessment with other available long-term satellite precipitation estimates. The results revealed that CORDEX-CORE RegCM4-7 does add value to its driving GCMs distributions. The results show a positive Added Value Coverage (AVC) of 36.45 to 59.9% for annual daily events, 25.69 to 56.97% for seasonal daily events, 40.97 to 51.74% for heavy annual daily events (above the 95th percentile), and 48.8 to 58.89% for very heavy annual daily events (above the 99th percentile) regardless of the driving GCMs at both high-resolution and low-resolution. The results also feature an overall non-significant AVC ranging from 17.21 to 52.99%, thus suggesting that CORDEX-CORE RegCM4-7 mostly does not alter distributional properties from the driving GCMs if no value could be added. Although differences in individual GCM-based RegCM4-7 outputs exist, the RCM's internal physics and sub-grid processes appeared to be more influential on the DAV results due to the similarity in high- and low-resolution results from the downscaled outputs. This hypothesis is supported by additional evidence from the consistency of the historical simulations results with the evaluation runs, and the strong spatial dependence of the results to inherent local forcing and processes governing regions where most of the Added Value (AV) is observed. Overall, the results indicate that, for its first attempt to produce high-resolution climate simulations at nearly 25 km resolution, CORDEX-CORE RegCM4-7 has achieved the promise of an AV, especially for extreme events over Africa, even if further improvements will still be needed.
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