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.

Global warming has intensified the risk of compound events, among which compound hot and dry events (CHDEs) have drawn considerable attention due to their high frequency and severe impacts. In this study, based on observational data and simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6), we compare the performance of indirect and direct emergent constraint approaches in projecting the frequency of global CHDEs. The results show that by the end of the 21st century, the frequency of CHDEs increases significantly over most global land areas, with the largest increases occurring in North America, South America, and Europe, while the increase is relatively smaller in eastern and southern Asia. Attribution analysis indicates that temperature rise is the primary driver of increased CHDEs frequency, whereas spatial variations in precipitation contribute to regional differences. The indirect constraint, based on global warming magnitude, effectively reduces projection uncertainty in most regions, with the largest reduction in uncertainty range reaching 37.4% (25.4%) for Shared Socioeconomic Pathways (SSP) 5-8.5 (SSP2-4.5) scenario. The direct constraint, established using the relationship between historical precipitation trends and future CHDEs frequency, can reduce the uncertainty range by up to 15.99% (14.87%) for SSP5-8.5 (SSP2-4.5) scenario. Overall, the indirect constraint demonstrates broader applicability and achieves greater reductions in projection uncertainty compared to the direct constraint.