In this paper we describe SEAS5, ECMWF's fifth generation seasonal forecast system, which became operational in November 2017. Compared to its predecessor, System 4, SEAS5 is a substantially changed forecast system. It includes upgraded versions of the atmosphere and ocean models at higher resolutions, and adds a prognostic sea-ice model. Here, we describe the configuration of SEAS5 and summarise the most noticeable results from a set of diagnostics including biases, variability, teleconnections and forecast skill. An important improvement in SEAS5 is the reduction of the equatorial Pacific cold tongue bias, which is accompanied by a more realistic El Nino amplitude and an improvement in El Nino prediction skill over the central-west Pacific. Improvements in 2m temperature skill are also clear over the tropical Pacific. Sea-surface temperature (SST) biases in the northern extratropics change due to increased ocean resolution, especially in regions associated with western boundary currents. The increased ocean resolution exposes a new problem in the northwest Atlantic, where SEAS5 fails to capture decadal variability of the North Atlantic subpolar gyre, resulting in a degradation of DJF 2m temperature prediction skill in this region. The prognostic sea-ice model improves seasonal predictions of sea-ice cover, although some regions and seasons suffer from biases introduced by employing a fully dynamical model rather than the simple, empirical scheme used in System 4. There are also improvements in 2m temperature skill in the vicinity of the Arctic sea-ice edge. Cold temperature biases in the troposphere improve, but increase at the tropopause. Biases in the extratropical jets are larger than in System 4: extratropical jets are too strong, and displaced northwards in JJA. In summary, development and added complexity since System 4 has ensured that SEAS5 is a state-of-the-art seasonal forecast system which continues to display a particular strength in the El Nino Southern Oscillation (ENSO) prediction.

利用北京气候中心（BCC）次季节-季节（Sub-seasonal to Seasonal， S2S）预测系统20年（1994 -2013年）回报试验数据， 在评估BCC S2S预测系统对中国西南地区夏季降水次季节预报性能基础上， 进而采用基于奇异值分解（Singular Value Decomposition， SVD）的误差订正方案对预测结果进行订正。结果表明： BCC S2S预测系统对西南地区夏季降水的次季节预报技巧随起报时间的提前不断下降， 在起报时间提前10天以内具有一定预报技巧， 而在起报时间提前10天以上基本无技巧， 同时存在明显的区域性和年际差异。采用SVD误差订正方案能够较好改善BCC S2S系统对西南地区夏季降水的次季节预测水平， 起报时间提前0~10、 11~20、 21~30天原始预测结果与观测间的异常相关系数分别为0.50， 0.31和0.25， 订正后分别提高至0.70， 0.75和0.70， 同时订正后的预测结果与观测间的空间相关系数在起报时间提前0~10天提高了0.3左右， 尤其对起报时间提前11~30天的预测结果改进更加明显， 空间相关系数提高了0.6左右。

The capability of the Beijing Climate Center （BCC） sub-seasonal to seasonal （S2S） prediction system in forecasting the summer precipitation in Southwestern China has been evaluated based on the 20-year （1994 -2013） hindcast data.Meanwhile， the singular value decomposition （SVD） bias correction scheme was subsequently adopted to improve the forecast skill of the BCC S2S prediction system.The results show that the skill of BCC S2S prediction system in forecasting the summer precipitation over Southwestern China was decreases continuously with the early forecast lead time.It shows certain forecasting skills within 10 days ahead of the starting time， but basically has no skills exceeding 10 days ahead of the starting time.In addition， the forecast skill exhibits obvious regional and inter-annual differences.The sub-seasonal prediction skill of the summer precipitation in Southwestern China could be well improved by adopting the SVD bias correction scheme.The abnormal correlation coefficients between the original prediction results and observations are 0.50， 0.31 and 0.25 with the forecast lead time of 0~10， 11~20， 21~30 days， which are increased to 0.70， 0.75 and 0.70 after the correction， respectively.Moreover， in comparison with the spatial correlation coefficient （SCC） between the bias original prediction results and observations， the SCC between the bias corrected prediction results and observations is increased by about 0.3 at the forecast lead time of 0~10 days， especially for the prediction results with the forecast lead time of 11~30 days， the spatial correlation coefficient for the bias corrected results is significantly increased by 0.6 compared to the original results.