An accurate description of changes in extreme rainfall events requires high resolution simulations. Regional climate models (RCMs), where GCM data are used to provide input boundary conditions, are widely used as a way to resolve finer spatial scale phenomena. A problem with this, however, is that the inherent systematic biases within the GCM simulation are transferred to the RCM through the model boundaries. In this work we focus on the impact of bias correction of lateral and lower boundary conditions on simulated extreme rainfall events. Here three bias correction approaches are investigated. In increasing order of complexity, these are corrections for the mean, mean and variance, and the nested bias correction (NBC) approach that also corrects for lag-1 autocorrelations at nested timescales. These corrections are implemented on six-hourly GCM data taken from the GCM simulations which are used to drive the RCM along the RCM lateral boundaries. To evaluate the performance of bias correction on simulation of extreme rainfall events, daily precipitation extremes indices from the World Meteorological Organization (WMO) Expert Team on Climate Risk and Sectoral Climate Indicators (ET-CRSCI) are used. The results show that bias correction on the boundary conditions produce the results in significant improvement in extremes indices. It is clear that sea surface temperature (SST) plays an important role in driving the simulation. The results indicate that within the domain (far from boundaries) the errors in precipitation extremes are strongly dependent on the RCM, with a smaller effect coming from changes in the lateral boundary conditions.

要准确描述极端降雨事件的变化，就需要高分辨率的模拟。使用GCM数据提供输入边界条件的区域气候模型（RCM）被广泛用作解决更精细的空间尺度现象的方法。但是，与此有关的一个问题是，GCM仿真中的固有系统偏差会通过模型边界转移到RCM。在这项工作中，我们重点研究横向和下边界条件的偏差校正对模拟极端降雨事件的影响。这里研究了三种偏差校正方法。以复杂度递增的顺序，这是对均值，均值和方差的校正，以及嵌套偏差校正（NBC）方法，该方法还可以在嵌套时间尺度上校正lag-1自相关。这些修正是基于从GCM模拟中获取的每六个小时的GCM数据进行的，这些数据用于沿RCM横向边界驱动RCM。为了评估偏差校正在极端降雨事件模拟中的性能，使用了世界气象组织（WMO）气候风险和部门气候指标专家组（ET-CRSCI）的每日极端降水指数。结果表明，对边界条件的偏差校正可显着改善极端指数。显然，海面温度（SST）在驱动模拟中起着重要作用。结果表明，在该区域内（远离边界），极端降水的误差主要取决于RCM，而较小的影响来自横向边界条件的变化。