For China, long-term changes are detected not only in the means of eight extreme precipitation indices, but also in their distribution shapes by quantile regression. This resulted in different trends for the means and other aspects of the index distributions. The differences between changes in the means and upper/lower extremes vary with region and index. A noteworthy feature is that changes in upper tails of the index distributions across a broad area, especially in the south, are at a much higher rate than mean trends estimated by the traditional linear regression model. This has practical implications for disaster risk management.
A B S T R A C TWe test a strategy for downscaling seasonal mean temperature for many locations within a region, based on principal component analysis (PCA), and assess potential benefits of this strategy which include an enhancement of the signalto-noise ratio, more efficient computations, and reduced sensitivity to the choice of predictor domain. These conditions are tested in some case studies for parts of Europe (northern and central) and northern China. Results show that the downscaled results were not highly sensitive to whether a PCA-basis or a more traditional strategy was used. However, the results based on a PCA were associated with marginally and systematically higher correlation scores as well as lower root-mean-squared errors. The results were also consistent with the notion that PCA emphasises the large-scale dependency in the station data and an enhancement of the signal-to-noise ratio. Furthermore, the computations were more efficient when the predictands were represented in terms of principal components.
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