An index sensitive to global warming, the standardized precipitation evapotranspiration index (SPEI), is employed in this study to construct a 1-km gridded multi-scalar drought index data bank in Taiwan. A site- and scale-dependent posterior fitness assessment procedure regarding determination of the most appropriate statistical distribution is used to standardize the station's water deficit/surplus series, thereby SPEI at various time scales. Model uncertainty at different scales is evaluated and the results show that the uncertainty is higher for the shorter 10-day scale. Contrasts in the climatic means between SPEI and popular standardized precipitation index (SPI) are compared. The climatic pre-summer monsoon heating and midsummer drought phenomena are better captured by the SPEI. The established data bank, one of the data integration tasks in the Taiwan Climate Change Projection and Information Platform (TCCIP) project, is helpful in historical drought diagnostics. It also serves as the ground truth to correct model biases while using the regional model to project hydro-climate change and impact assessment, and historical baseline in the statistical downscaling while developing the statistical relationship between the general circulation model outputs and fine-scale observations. As an example of applications, the gridded SPEI at 3-month time scale is used to study the interannual variability in springtime drought. The results show that Taiwan's southwestern plains region is most vulnerable to the risk of droughts. Composite analysis reveals that possible causes of island-wide drought link to the turnabout of cold El Niño-Southern Oscillation (ENSO) phase but also to the interannual variability in Pacific Decadal Oscillation when the analysis is initiated from the regional perspective.
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