Here we test a maximum evaporation theory that acknowledges the interdependence between radiation, surface temperature, and evaporation over saturated land. We show that the maximum evaporation approach recovers observed evaporation and surface temperature under non-water-limited conditions across a broad range of bio-climates. The implication is that the maximum evaporation concept can be used to predict potential evaporation that has long been a major difficulty for the hydrological community.

Many previous studies using offline drought indices report that future warming will increase worldwide drought. However, this contradicts observations/projections of vegetation greening and increased runoff. We resolved this paradox by re-calculating the same drought indices using direct climate model outputs and find no increase in future drought as the climate warms. We also find that accounting for the impact of CO₂ on plant transpiration avoids the previous overestimation of drought.

We focus on the initial analysis of inter-annual variability in the global terrestrial water cycle, which is key to understanding hydro-climate extremes. We find that (1) the partitioning of inter-annual variability is totally different with the mean state partitioning; (2) the magnitude of covariances can be large and negative, indicating the variability in the sinks can exceed variability in the source; and (3) the partitioning is relevant to the water storage capacity and snow/ice presence.

Recent experimental evidence suggests that during heat extremes, trees may reduce photosynthesis to near zero but increase transpiration. Using eddy covariance data and examining the 3 days leading up to a temperature extreme, we found evidence of reduced photosynthesis and sustained or increased latent heat fluxes at Australian wooded flux sites. However, when focusing on heatwaves, we were unable to disentangle photosynthetic decoupling from the effect of increasing vapour pressure deficit.

We focused on seeking for a decomposition approach which can produce independent decomposed components as a basis for understanding variability in hydro-climatic time series. We find that the rarely adopted two-way ANOVA model in hydro-climatic variability (rather than the traditional methods using linear trend or moving average) will produce independent components. This method is further applied to explore the variability in global precipitation and is expected to be useful for other variables.