A systematic analysis of climate model precipitation in southern Brazil

Climate model precipitation is the foremost input for hydrological models in climate change risk assessment. However, some aspects of precipitation (e.g., frequency, seasonality, and extremes) are usually not well represented by climate models, especially at the regional scale and in the tropics. In this study, we use a set of well-established metrics to evaluate the marginal, temporal, and spatial aspects of CMIP5 and CMIP6 precipitation in Southern Brazil. This region is in the transition between tropical and subtropical climates with diverse rainfall generation mechanisms and complex topography. We compare the multi-model-ensemble mean (MME) and a constrained ensemble (CE) of CMIP5 and CMIP6 against a high-resolution precipitation data grid. The constrained ensemble is obtained using a weighting approach that minimizes the difference between the simulated and observed cumulative distribution functions. We find that CMIP6 outperforms CMIP5 for most metrics, especially in the simulation of the seasonal cycle and the spatial distribution of precipitation. Simulated precipitation is more seasonal and more spatially dependent than the observations, with a dry bias characterized by lower precipitation amounts and higher consecutive dry days. Our analysis suggests that the models are not able to reproduce the transition between tropical and subtropical climates in this region as well as the passage of frontal systems. Future studies using CMIP6 should focus on those regional mechanisms of precipitation variability.

Automated summary

The study demonstrates that CMIP6 outperforms CMIP5 in simulating the seasonal cycle and spatial distribution of precipitation, but the simulated precipitation is more seasonal and spatially dependent than observations, with a dry bias.