Systematic Global Evaluation of Seasonal Climate Forecast Skill for Monthly Precipitation of JMA/MRI-CPS2 Compared with a Statistical Forecast System Using Climate Indices

This study aimed to systematically and globally evaluate the monthly precipitation forecasts of Japan Meteorological Agency/Meteorological Research Institute-Coupled Prediction System ver. 2 (JMA/MRI-CPS2), a dynamical seasonal climate forecast (Dyn-SCF) system operated by the Japan Meteorological Agency, by comparing its forecasts with those of a statistical SCF (St-SCF) system using climate indices. We developed a new global StSCF system using 17 climate indices and compared the monthly precipitation of this system with those of JMA/ MRI-CPS2. Consequently, the skill of JMA/MRI-CPS2 was determined to be globally higher than that of the St-SCF for zero-month lead forecasts. Contrarily, for forecasts made with a lead time of 1 month or longer, the deterministic skill of JMA/MRI-CPS2 was comparable to that of the St-SCF, and the probabilistic skill of JMA/ MRI-CPS2 remained slightly higher. In addition to evaluating the skill of JMA/MRI-CPS2, we identified several regions and seasons, for which JMA/MRI-CPS2 exhibited a low forecast skill, compared with the St-SCF. This indicated that JMA/MRI-CPS2 cannot sufficiently reproduce certain dynamics. In conclusion, comparing DynSCFs with St-SCFs can elucidate the potential regions and seasons to improve the forecast skill of Dyn-SCFs.

Automated summary

This study compared the monthly precipitation forecasts of two different systems, JMA/MRI-CPS2 and St-SCF, and found that JMA/MRI-CPS2 had higher skill for zero-month lead forecasts, but comparable skill for longer lead time forecasts. It also identified regions and seasons where JMA/MRI-CPS2 performed poorly compared to St-SCF, indicating the need for improvement in certain dynamics.