A bias correction method for precipitation through recognizing mesoscale precipitation systems corresponding to weather conditions

Accurate estimations of local precipitation are necessary for assessing water resources and water-related disaster risks. Numerical models typically used to estimate precipitation, but biases can result from insufficient resolution incomplete physical processes. To correct these biases, various bias correction methods have been developed. Recently, using machine learning developed improved performance. However, estimating hourly characteristics remains difficult due the nonlinearity precipitation. Here, we focused on systems that could be reproduced by numerical models, estimated spatial distribution recognizing relationship between simulated observed with a 0.06 degrees method. We subsequently applied quantile mapping method modify amounts. Validation showed our significantly reduce in simulations, especially frequency. temporal did not improve. Spatial autocorrelation analysis this predict scales 2500 40000 km 2 , which associated large-scale disturbances (e.g., cold fronts, warm low-pressure systems). The high accuracy estimates indicates frequency is strongly dependent scales. Accordingly, shows strong, relationship, accurately estimated.