A Hybrid Bias-Correction Framework for Extreme Precipitation in Convection-Permitting Models

Convection-permitting models (CPMs) better represent sub-daily precipitation than coarser models, but they still exhibit substantial biases in low probability occurrence extremes, with elevation-dependent patterns. In addition, the relatively short simulation periods, typically around 10 years, limit the robust estimation of rare events. This constrains the direct use of raw CPM output for applications that depend on extreme-value statistics. To address these limitations, this study introduces a hybrid bias-correction framework for CPM precipitation that targets hourly resolution.

The proposed method combines non-parametric and parametric components within an elevation-based pooling strategy. Stations and co-located CPM grid cells are grouped into elevation bands, and a common, monthly varying correction is estimated for each band to represent both spatial and seasonal variability. Low-to-moderate precipitation intensities are corrected using robust empirical quantile mapping. The upper tail is adjusted using an optimized Weibull tail model with left censoring, inspired by the Simplified Metastatistical Extreme Value approach. The optimal threshold is searched within the 0.8 to 0.97 quantile range using an adjusted Weibull tail test.

Model performance is evaluated using both extreme-value and distributional metrics derived from observations, raw CPM output, and bias-corrected series. Extreme behavior is assessed through 20-year return levels of 1-hour and 24-hour precipitation. Distributional performance is quantified using mean absolute bias computed over empirical quantiles, allowing improvements to be tracked across the full range of precipitation intensities.
Robustness is examined through a structured validation framework. Spatial robustness is tested by evaluating the elevation-based pooling approach using k-fold schemes in which subsets of stations are withheld from calibration. Temporal robustness is assessed through repeated cross-validation on the 10-year CPM slices, with six years randomly assigned to calibration and four years to validation.

Preliminary results show a reduction in mean absolute bias after correction, largely driven by an improved representation of the wet-hour ratio. When a minimum rainfall threshold is applied to the raw CPM data, the bias becomes comparable to that of the bias-corrected output, indicating that drizzle remains a key issue. For extremes, biases in 1-hour 20-year return levels generally decrease but are not fully eliminated, reflecting the large uncertainty in the distribution upper tail. For 24-hour 20-year return levels, results are mixed: biases are reduced for some CPMs but introduced or amplified for others, highlighting model-specific differences in the spatial characteristics of storm structure and organization. The validation indicates that the elevation-based pooling yields spatially robust corrections for sufficiently small, climatically homogeneous domains, while the assessment of temporal robustness remains inconclusive due to the limited length of the available 10-year CPM simulations.