The article by Wu et al. addresses the challenges of defining and extracting trends from nonlinear and nonstationary time series data, which are common in climate studies and other scientific fields. The authors propose a new definition of "trend" as an intrinsically determined monotonic function within a specific temporal span, with at most one extremum. This definition emphasizes the adaptability and naturalness of the trend extraction process, suggesting the use of the Empirical Mode Decomposition (EMD) method for this purpose. EMD is described as an adaptive and empirical technique that decomposes data into intrinsic mode functions (IMFs), which can be used to extract the trend and detrend the data. The article demonstrates the application of this method to annual global surface air temperature anomalies (GSTA) data, showing that it can reveal meaningful trends and variability on various time scales. The results highlight the importance of considering the local time scale associated with the trend and the robustness of the EMD method in handling nonstationary and nonlinear data. The study concludes that the EMD-based approach provides a more accurate and reliable method for trend extraction and variability analysis compared to traditional methods.The article by Wu et al. addresses the challenges of defining and extracting trends from nonlinear and nonstationary time series data, which are common in climate studies and other scientific fields. The authors propose a new definition of "trend" as an intrinsically determined monotonic function within a specific temporal span, with at most one extremum. This definition emphasizes the adaptability and naturalness of the trend extraction process, suggesting the use of the Empirical Mode Decomposition (EMD) method for this purpose. EMD is described as an adaptive and empirical technique that decomposes data into intrinsic mode functions (IMFs), which can be used to extract the trend and detrend the data. The article demonstrates the application of this method to annual global surface air temperature anomalies (GSTA) data, showing that it can reveal meaningful trends and variability on various time scales. The results highlight the importance of considering the local time scale associated with the trend and the robustness of the EMD method in handling nonstationary and nonlinear data. The study concludes that the EMD-based approach provides a more accurate and reliable method for trend extraction and variability analysis compared to traditional methods.