The ability to estimate current GDP growth before official data are released, known as nowcasting, is crucial for the Chinese government to effectively implement economic policy and manage economic uncertainties; however, there is limited research on nowcasting China's GDP in a data-rich environment. We evaluate the performance of various machine learning algorithms, dynamic factor models, static factor models, and MIDAS regressions in nowcasting the Chinese annualised real GDP growth rate in pseudo out-of-sample exercise, using 89 macroeconomic variables from years 1995 to 2020. We find that some machine learning methods outperform the benchmark dynamic factor model. The machine learning method that deserves more attention is ridge regression, which dominates all other models not only in terms of nowcast error but also in effective recognition of the impacts of the Global Financial Crisis and Covid-19 shocks. Policy-wise, our study guides practitioners in selecting appropriate nowcasting models for China's macroeconomy.