Performance of gauge-based and reanalysis gridded temperature datasets in representing means and extremes across different climate zones of the Brazos River Basin, United States

Abstract

Introduction Identifying the most reliable gridded temperature datasets for each region is crucial for supporting evidence-based decision-making. Methods This study evaluates the performance of gauge-based and reanalysis gridded temperature datasets, Daymet, PRISM, MERRA, and ERA5, in estimating means and extremes across the diverse climate zones of the Brazos River Basin in the United States. The evaluation spans 1998–2020 and examines the datasets’ ability to estimate maximum (Tmax) and minimum (Tmin) temperatures across daily, monthly, and annual scales. Additionally, the datasets’ ability to estimate temperature extremes are assessed using 12 indices recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI). The evaluations were conducted using the Global Historical Climatology Network (GHCN) as the reference dataset. Four continuous statistical metrics were used to assess performance, and the overall ranking was determined using the Comprehensive Rating Index (CRI). Results and discussion The results indicate that dataset performance varies across climate zones, temporal scales, and specific temperature extremes. PRISM and Daymet demonstrated the highest accuracy in estimating daily Tmax and Tmin, respectively in most climate zones. For monthly and annual time scales, Daymet was most effective for estimating Tmax, and PRISM for Tmin. Conversely, MERRA showed the weakest performance for Tmax, and ERA5 for Tmin, across all temporal scales. In certain climate zones, reanalysis datasets are better than gauge-based datasets. For temperature extremes, PRISM outperformed the other datasets across most indices, while ERA5 showed the poorest performance for most of the indices. Thus, the study recommends selecting the highest-performing dataset for Tmax and Tmin separately, tailored to the temporal scale relevant to the study’s objectives.