Unveiling Precipitation Trend Characteristics in Changing Poorly-gauged Regions: Leveraging Alternative Raster Sources

The performance of the Fifth generation of the European Centre for Medium-Range Weather Forecasts Reanalysis (ERA5), the land component of ERA5 (ERA5-Land), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR), and Global Precipitation Climatology Center (GPCC) was evaluated in characterizing various precipitation trend characteristics. The analyses were conducted using 817 rain gauges distributed across a diverse range of climatic conditions throughout Iran. GPCC exhibited superior performance compared to other alternatives in identifying trend directions, correctly recognizing annual, autumnal, wintertime, and springtime precipitation trends in 75.1%, 75.0%, 85.5%, and 61.7% of the studied areas, respectively. Moreover, GPCC outperformed the other options in detecting the significance level of trends. CHIRPS performed less reliably, detecting an overall false positive trend. CHIRPS exhibited an upward trend for heavy precipitation events, contradicting to observed decreasing trends. As for the slope quantity, all alternatives provided unsatisfactory estimates. This indicates that while GPCC can depict the climate change impacts, it failed to provide reliable estimates for the magnitude of changes. These findings underscore that determining the absolute value of a variable does not necessarily guarantee suitability for other analyses. The long-term datasets of GPCC, ERA5, and ERA5-Land were analyzed for 1972–2001 and 1988–2017, revealing that despite poorer performance in the earlier period, they accurately depicted the direction of change in most cases. The results suggest that gridded datasets can accurately portray the effects of climatic changes on precipitation under data limitation.