Torrential rainfall associated with linear precipitation systems occurred in Northern Kyushu, Japan, during July 5–6, 2017, causing severe damage in Fukuoka and Oita Prefectures. According to our statistical survey using ground rain gauges, the torrential rainfall was among the heaviest in recorded history for 6- and 12-h accumulated rainfall, and was unusual because heavy rain continued locally for nine hours. The predictability of precipitation associated with linear precipitation systems for this event was investigated using a cloud-resolving numerical weather prediction model with a horizontal grid interval of 1 km. The development of multiple linear precipitation systems was predicted in experiments whose initial calculation time was from several hours to immediately before the torrential rain (9:00, 10:00, 11:00, and 12:00 Japan Standard Time on July 5), although there were some displacement errors in the predicted linear precipitation systems. However, the stationary linear precipitation systems were not properly predicted. The predictions showed that the linear precipitation systems formed one after another and moved eastwards. In the relatively accurate prediction whose initial time was 12:00 on July 5, immediately before the torrential rainfall began, the forecast accuracy was evaluated using the 6-h accumulated precipitation (P6h) from 12:00 to 18:00 on July 5, the period of the heaviest rainfall. The average of the P6h in an area 100 km×40 km around the torrential rainfall area was nearly the same for the analysis and the prediction, indicating that the total precipitation amount around the torrential rainfall area was predictable. The result of evaluating the quantitative prediction accuracy using the Fractions Skill Score (FSS) indicated that a difference in location of 25 km (50 km) or greater should be allowed for in the models to produce useful predictions (those defined as having an FSS ≥0.5) for the accumulated rainfall of P6h ≥50 mm (150 mm). The quantitative prediction accuracy examined in this study can be basic information to investigate the usage of predicted precipitation data.

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