Reliable estimation of return period values of extreme precipitation at ungauged locations is considered to be a key exercise in hydrometeorological studies. This study aims to identify an accurate approach in producing spatial maps (i.e. ungauged estimation) of extreme precipitation for any return period within a region. The study compares the following approaches: interpolation of summary of data as represented by L-moments, interpolation of parameters of an extreme value distribution and interpolation of return period quantile value. Several interpolation schemes are considered; however, the aim is to evaluate schemes that employ secondary data. The schemes compared are ordinary kriging, kriging with external drift (KED) and a more traditional, inverse distance weighting. The secondary data namely elevation, satellite based mean annual precipitation (MAP), distance from nearest coast (CD) and geographical coordinates are incorporated in the KED system. Annual maximum 1-day precipitation series at 76 gauging stations from the region of East China have been used to assess the performance. The generalized extreme value (GEV) distribution, appropriate for the study region, with the method of L-moments is used to analyze the frequency of extreme precipitation. It is found that either the approach of interpolating parameters of GEV distribution or L-moments should be the natural choice for estimating design value at any return period. However, in terms of error statistics the approach of interpolating parameters has given a lower RMSE value compared to the approach of interpolating L-moments. The approach of quantile interpolation performed worst and should not be used in practice in interpolating return period values. The KED is recognized as the most appropriate interpolation scheme when a significant covariate is identified. The MAP appears to be a suitable covariate in most cases when interpolating L moments (1st and 2nd L-moment) or GEV parameters (location and scale parameter). There is no spatial dependence identified for L-skewness or shape parameter of GEV distribution and in the future one should concentrate on how a superior spatial model can be identified in this context.

在无气象的地方，可靠地估算极端降水的返回期值是水文气象研究中的一项重要工作。这项研究的目的是确定在区域内任何回归期产生极端降水的空间图（即无量估计）的准确方法。研究比较了以下方法：以L矩表示的数据摘要的插值，极值分布的参数的插值和返回周期分位数的插值。考虑了几种插值方案；但是，目的是评估采用辅助数据的方案。比较的方案是普通克里金法，带外部漂移的克里金法（KED）和更传统的逆距离加权。次要数据即海拔 在KED系统中结合了基于卫星的年平均降水量（MAP），距最近海岸的距离（CD）和地理坐标。使用华东地区76个测量站的年度最大1天降水系列来评估性能。适用于研究区域的广义极值（GEV）分布，采用L矩方法，用于分析极端降水的频率。发现在任何返回期估算GEV分布的参数值的方法或L矩都应该是自然的选择。然而，就误差统计而言，内插参数的方法相比于内插L矩的方法具有较低的RMSE值。分位数插值方法表现最差，在实践中不应该在插值返回周期值中使用。当识别出重要的协变量时，KED被认为是最合适的插值方案。在内插L矩（第一和第二L矩）或GEV参数（位置和比例参数）时，MAP在大多数情况下似乎是合适的协变量。目前还没有发现关于GEV分布的L偏度或形状参数的空间依赖性，将来人们应该集中精力研究如何在这种情况下识别出更好的空间模型。在内插L矩（第一和第二L矩）或GEV参数（位置和比例参数）时，MAP在大多数情况下似乎是合适的协变量。目前还没有发现关于GEV分布的L偏度或形状参数的空间依赖性，将来人们应该集中精力研究如何在这种情况下识别出更好的空间模型。在大多数情况下，对L矩（第一和第二L矩）或GEV参数（位置和比例参数）进行插值时，MAP似乎是合适的协变量。目前还没有发现关于GEV分布的L偏度或形状参数的空间依赖性，将来人们应该集中精力研究如何在这种情况下识别出更好的空间模型。