TReAD: 40-year Taiwan Reanalysis Downscaling Dataset at 2 km

Abstract. There has been an increasing demand to reconstruct long-term gridded Essential Climate Variables (ECVs) for climate-change impact and application studies, especially for the regions with highly complex topography like Taiwan. Furthermore, the need for climatic variables like wind speed, relative humidity, etc has been rapidly increasing for diverse application research mainly because sparsely distributed station-based observation data cannot fulfill the requirement. This paper introduces the Taiwan Reanalysis Downscaling data (TReAD) that re-forecasts a range of ECVs with 2-km resolution for Taiwan from 1980 to 2019. Forced with the European ReAnalysis (ERA5), TReAD was generated by dynamically downscaling simulations using the Weather Research and Forecasting (WRF) model. Overall, TReAD shows a comparable long-term trend seen in the ground-based observation data while biases can still be seen mostly in mountainous regions. Moreover, the ranking of the six ECVs in terms of overall performance (taking into account correlation coefficients and deviations) is atmospheric pressure, temperature, relative humidity, shortwave radiation, wind velocity and precipitation. As for reproducing extreme weather events such as typhoons, TReAD reasonably captured the temporal and spatial variability of precipitation observed for Morakot in 2009. To our knowledge, TReAD is the first high-resolution long-term historical climate reconstruction for Taiwan and has been applied in climate-related studies. For instance, TReAD has been found to be a valuable (higher weights) data source particularly in gauge-scarce areas when developing a multi-source precipitation product in the hydroclimatic field. What’s more, the impact of typhoons on cloud forests has been assessed by using TReAD’s wind velocity that could not be achieved using gauge-based observation data alone. It is important to highlight that TReAD not only can resolve the effect of the complex terrain in Taiwan but also make many more climate variables available for the foreseen climate-related studies. The datasets are freely available for download via the SciDM platform (Cheng et al., 2025, https://doi.org/10.30193/scidm-rs-2576295).