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Gridded data sets that are widely used to characterize recent historical trends in regional and global climate are derived from a temporally varying and spatially inhomogeneous observational network. Lin and Huybers (2018, https://doi.org/10.1029/2018GL079709) demonstrate that such network variations underlying two widely used precipitation data sets have biased trends in mean and extreme rainfall over India. I highlight similar concerns raised by studies over other regions and discuss the implications for climate change research. Evaluating uncertainties arising from such nonclimatic factors requires access to underlying station data that is currently unavailable for several vulnerable regions but is critical for accurately characterizing recent climate change and evaluating climate models.

Plain Language Summary Gridded climate data sets derived from weather stations provide spatially complete estimates of weather conditions and are often used to analyze climate trends. The density of weather stations, which provide point-based measurements, varies across the globe. In addition, the network of weather stations has undergone various changes over the years. Could trends in climate variables based on gridded data sets that utilize this changing network be subject to biases resulting from these changes? A recent article by Lin and Huybers (2018) proposes a method to quantify these biases using spatially uniform satellite measurements and information about the underlying station network. They apply their method to highlight potentially substantial biases in reported trends of mean and extreme precipitation during the Indian summer monsoon season, an area of intensive research. Their work, along with other studies of global and regional data sets that address similar issues, underscores the importance of carefully examining the artificial trends and uncertainties induced by such network inhomogeneity issues in commonly used data sets and continuing efforts to improve these data sets to better quantify recent climate change. However, these efforts are limited by the unavailability of raw station data from several institutions in critical regions of the world.