Changes in the patterns of tropical precipitation (P) and circulation are analyzed in Coupled Model Intercomparison Project phase 5 (CMIP5) GCMs under the representative concentration pathway 8.5 (RCP8.5) scenario. A robust weakening of the tropical circulation is seen across models, associated with a divergence feedback that acts to reduce convection most in areas of largest climatological ascent. This is in contrast to the convergence feedback seen in interannual variability of tropical precipitation patterns. The residual pattern of convective mass-flux change is associated with shifts in convergence zones due to mechanisms such as SST gradient change, and this is often locally larger than the weakening due to the divergence feedback. A simple framework is constructed to separate precipitation change into components based on different mechanisms and to relate it directly to circulation change. While the tropical mean increase in precipitation is due to the residual between the positive thermodynamic change due to increased specific humidity and the decreased convective mass flux due to the weakening of the circulation, the spatial patterns of these two components largely cancel each other out. The rich-get-richer mechanism of greatest precipitation increases in ascent regions is almost negated by this cancellation, explaining why the spatial correlation between climatological P and the climate change anomaly ΔP is only 0.2 over the tropics for the CMIP5 multimodel mean. This leaves the spatial pattern of precipitation change to be dominated by the component associated with shifts in convergence zones, both in the multimodel mean and intermodel uncertainty, with the component due to relative humidity change also becoming important over land.

Understanding observed changes to the global water cycle is key to predicting future climate changes and their impacts. While many datasets document crucial variables such as precipitation, ocean salinity, runoff, and humidity, most are uncertain for determining long-term changes. In situ networks provide long time series over land, but are sparse in many regions, particularly the tropics. Satellite and reanalysis datasets provide global coverage, but their long-term stability is lacking. However, comparisons of changes among related variables can give insights into the robustness of observed changes. For example, ocean salinity, interpreted with an understanding of ocean processes, can help cross-validate precipitation. Observational evidence for human influences on the water cycle is emerging, but uncertainties resulting from internal variability and observational errors are too large to determine whether the observed and simulated changes are consistent. Improvements to the in situ and satellite observing networks that monitor the changing water cycle are required, yet continued data coverage is threatened by funding reductions. Uncertainty both in the role of anthropogenic aerosols and because of the large climate variability presently limits confidence in attribution of observed changes.

The primary objective of CFMIP is to inform future assessments of cloud feedbacks through improved understanding of cloud-climate feedback mechanisms and better evaluation of cloud processes and cloud feedbacks in climate models. However, the CFMIP approach is also increasingly being used to understand other aspects of climate change, such as nonlinear change and regional changes in atmospheric circulation and precipitation. CFMIP is supporting ongoing model inter-comparison activities by coordinating a hierarchy of targeted experiments for CMIP6, along with a set of cloud related output diagnostics. CFMIP contributes primarily to addressing the CMIP6 questions "How does the Earth System respond to forcing?" and "What are the origins and consequences of systematic model biases?" and supports the activities of the WCRP Grand Challenge on Clouds, Circulation and Climate Sensitivity. A compact set of Tier 1 experiments is proposed for CMIP6 to address the question: "(1) What are the physical mechanisms underlying the range of cloud feedbacks and cloud adjustments predicted by climate models, and which models have the most credible cloud feedbacks?" Additional Tier 2 experiments are proposed to address the following questions: (2) Are cloud feedbacks consistent for climate cooling and warming, and if not, why? (3) How do cloud-radiative effects impact the structure, the strength and the variability of the general atmospheric circulation in present and future climates? (4) How do responses in the climate system due to changes in solar forcing differ from changes due to CO2, and is the response sensitive to the sign of the forcing? (5) To what extent is regional climate change per CO2 doubling state-dependent (nonlinear), and why? (6) Are climate feedbacks during the 20th century different to those acting on long term climate change and climate sensitivity? (7) How do regional climate responses (e.g. in precipitation) and their uncertainties in coupled models arise from the combination of different aspects of CO2 forcing and sea surface warming? CFMIP also proposes a number of additional model outputs in the CMIP DECK, CMIP6 Historical and CMIP6 CFMIP experiments, including COSP simulator outputs and process diagnostics to address the following questions: (1) How well do clouds and other relevant variables simulated by models agree with observations? (2) What physical processes and mechanisms are important for a credible simulation of clouds, cloud feedbacks and cloud adjustments in climate models? (3) Which models have the most credible representations of processes relevant to the simulation of clouds? (4) How do clouds and their changes interact with other elements of the climate system

Projected changes in regional seasonal precipitation due to climate change are highly uncertain, with model disagreement on even the sign of change in many regions. Using a 20-member CMIP5 ensemble under the RCP8.5 scenario, the intermodel uncertainty of the spatial patterns of projected end-of-twenty-first-century change in precipitation is found not to be strongly influenced by uncertainty in global mean temperature change. In the tropics, both the ensemble mean and intermodel uncertainty of regional precipitation change are found to be predominantly related to spatial shifts in convection and convergence, associated with processes such as sea surface temperature (SST) pattern change and land-sea thermal contrast change. The authors hypothesize that the zonal-mean seasonal migration of these shifts is driven by 1) the nonlinear spatial response of convection to SST changes and 2) a general movement of convection from land to ocean in response to SST increases. Assessment of tropical precipitation model projections over East Africa highlights the complexity of regional rainfall changes. Thermodynamically driven moisture increases determine the magnitude of the long rains (March-May) ensemble mean precipitation change in this region, whereas model uncertainty in spatial shifts of convection accounts for almost all of the intermodel uncertainty. Moderate correlations are found across models between the long rains precipitation change and patterns of SST change in the Pacific and Indian Oceans. Further analysis of the capability of models to represent present-day SSTrainfall links, and any relationship with model projections, may contribute to constraining the uncertainty in projected East Africa long rains precipitation.

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