From Local to Field Significance: A Comparative Analysis of Trend Detection Methods for Mean and Extreme Precipitation Over China

The detectability of intensified mean and extreme precipitation across China remains a topic of debate among experts due to the low signal-to-noise ratio in precipitation. Assessing the significance of regional precipitation trend requires simultaneous significance tests across multiple locations within the region (field significance test). This study evaluates the statistical power of four field significance tests—Walker test, false discovery rate (FDR) test, regression pattern (RP) test, and Counting test—to measure their ability to correctly reject a false null hypothesis. Thus, two realizations of mean and extreme precipitation fields were constructed—one with only internal variability and one with both internal variability and external forcing—using initial-condition large-ensemble simulations. It is found that the Counting test has a stronger ability to detect trend signals in low signal-to-noise ratio features, such as precipitation, compared to other methods. When the optimal field significance tests were applied to observed mean and extreme precipitation fields, the intensification of both mean and extreme precipitation in China has become field significant, with the extreme trends detected earlier than those for mean precipitation.