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Assessing Biases in CMA-MESO Summer Precipitation Forecasts over East China
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Hao MIAO1, 2, Kefeng ZHU1, 2, Quan YANG1, 2, Wenru SHI1, 2, Weiguang LIU1, 2, Xin LI1, 2
Chinese Journal of Atmospheric Sciences | 2026, 50(4) : 1205 - 1220
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Chinese Journal of Atmospheric Sciences | 2026, 50(4): 1205-1220
• Articles •
Assessing Biases in CMA-MESO Summer Precipitation Forecasts over East China
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Hao MIAO1, 2, Kefeng ZHU1, 2, Quan YANG1, 2, Wenru SHI1, 2, Weiguang LIU1, 2, Xin LI1, 2
Affiliations
  • 1Nanjing Innovation Institute for Atmospheric Sciences, Chinese Academy of Meteorological Sciences-Jiangsu Meteorological Service, Nanjing 210041
  • 2Jiangsu Key Laboratory of Severe Storm Disaster Risk/Key Laboratory of Transportation Meteorology of China Meteorological Administration, Nanjing 210041
Published: 2026-07-15 doi: 10.3878/j.issn.1006-9895.2506.25060
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The China Meteorological Administration Mesoscale Model (CMA-MESO) serves as a critical operational system for precipitation forecasting. However, its eight-times-daily rolling updates present new challenges for forecast evaluation. A comprehensive understanding of mean bias characteristics, variations across different forecast lead times, and error sources is key to model improvement. This study systematically evaluated the precipitation forecast performance of the CMA-MESO over East China during the summer of 2023. The key findings are as follows: (1) While the averaged multiple forecasts generally reproduced the spatial distribution patterns of observed precipitation, systematic biases existed in precipitation intensity and frequency. (2) The model significantly overestimated the precipitation amount for moderate-and-above rainfall events, with 3.7% underestimation of the frequency of no-rainfall events but 2.6% overestimation of the frequency of light rain events, along with elevated false alarm rates for heavy-and-above rainfall events. (3) The forecast performance showed a distinct lead-time dependence, with precipitation biases largest in 3~6 h nowcasting, gradually decreasing to a minimum in 21~24 h forecasts, and then increasing again in the 24~36 h range. (4) Physical analysis revealed that the nowcasting overestimation stemmed from excessive hydrometeor introduction during cloud analysis, and the overestimation in the forecast after 24 h resulted from excessive low-level wind speeds that promoted moisture transport to the target region.

CMA-MESO model  /  Precipitation forecast  /  Multiple forecasts  /  Bias causes
缪昊, 朱科锋, 杨全, 史文茹, 刘伟光, 李昕. CMA-MESO模式对华东地区夏季降水预报偏差分析. 大气科学, 2026 , 50 (4) : 1205 -1220 . DOI: 10.3878/j.issn.1006-9895.2506.25060
Hao MIAO, Kefeng ZHU, Quan YANG, Wenru SHI, Weiguang LIU, Xin LI. Assessing Biases in CMA-MESO Summer Precipitation Forecasts over East China[J]. Chinese Journal of Atmospheric Sciences, 2026 , 50 (4) : 1205 -1220 . DOI: 10.3878/j.issn.1006-9895.2506.25060
Year 2026 volume 50 Issue 4
217
100
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Article Info
doi: 10.3878/j.issn.1006-9895.2506.25060
  • Receive Date:2025-04-09
  • Online Date:2026-09-30
  • Published:2026-07-15
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  • Received:2025-04-09
Affiliations
    1Nanjing Innovation Institute for Atmospheric Sciences, Chinese Academy of Meteorological Sciences-Jiangsu Meteorological Service, Nanjing 210041
    2Jiangsu Key Laboratory of Severe Storm Disaster Risk/Key Laboratory of Transportation Meteorology of China Meteorological Administration, Nanjing 210041
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
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属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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