Improved estimates of future precipitation change in climate models

Future change in precipitation driven by anthropogenic influences on the Earth’s radiative balance will further affect ecosystems, water resources, agriculture, economies, lives and livelihoods. Increased clarity on anthropogenically forced precipitation change can assist adaptation in some contexts. Climate scientists typically quantify precipitation change in models using the average value of the percentage change evident in many different models, i.e., $$\% \Delta {P}^{j}=100\left(\,\frac{{{P}_{2}}^{j}-{{P}_{1}}^{j}}{{{P}_{1}}^{j}}\right)$$ , where $${P}_{i}^{j}$$ is the average value of precipitation over Period $$i$$ in model $$j$$ . Here we use theory and results from CMIP6 climate models under preindustrial, historical and future forcing to assess the accuracy of this approach. We show that this standard approach inaccurately estimates precipitation change evident in models, even in infinitely large ensembles. Under a wide variety of circumstances, the discrepancy is approximated by $$100({\mu }_{2}/{\mu }_{1})/(m/{{{\rm {CoV}}}}^{2}-1)$$ , where $${\mu }_{i}$$ is the population mean for Period $$i$$ , $$m$$ is the number of years in the reference period, and CoV is the Coefficient of Variation (i.e., the standard deviation of precipitation variability divided by the mean). The discrepancy is therefore greater for shorter reference periods and is greatest where the $${CoV}$$ is large (which tends to occur in dry regions) and anthropogenic forcing increases precipitation. The discrepancy using climate model output under SSP370 forcing has an average value of 5.7% over the tropics in December–January–February, with far greater values in many subregions. Alternative approaches to quantifying precipitation change are described.