Do CMIP models capture long-term observed annual precipitation trends?

This study provides a long-term (1891-2014) global assessment of precipitation trends using data from two station-based gridded datasets and climate model outputs evolved through the fifth and sixth phases of the Coupled Model Intercomparison Project (CMIP5 and CMIP6, respectively). Our analysis employs a variety of modeling groups that incorporate low- and high-top level members, with the aim of assessing the possible effects of including a well-resolved stratosphere on the model's ability to reproduce long-term observed annual precipitation trends. Results demonstrate that only a few regions show statistically significant differences in precipitation trends between observations and models. Nevertheless, this pattern is mostly caused by the strong interannual variability of precipitation in most of the world regions. Thus, statistically significant model-observation differences on trends (1891-2014) are found at the zonal mean scale. The different model groups clearly fail to reproduce the spatial patterns of annual precipitation trends and the regions where stronger increases or decreases are recorded. This study also stresses that there are no significant differences between low- and high-top models in capturing observed precipitation trends, indicating that having a well-resolved stratosphere has a low impact on the accuracy of precipitation projections.

Automated summary

This study provides a long-term global assessment of precipitation trends from 1891 to 2014 using data from station-based gridded datasets and climate models. Results show that only a few regions exhibit statistically significant differences in precipitation trends between observations and models, mostly due to strong interannual variability. The modeling groups fail to accurately reproduce the spatial patterns of annual precipitation trends.