Evaluation of the climatic water balance (CWB) – i.e. precipitation minus potential evapotranspiration – has strong potential as a tool for investigating patterns of variability and change in the water cycle since it estimates the (im)balance of atmospheric moisture near the land surface. Using observations from a middle-Himalaya weather station at Mukteshwar (29.474°N, 79.646°E, Uttarakhand state) in India, we demonstrate a CWB-based set of analytical procedures can robustly characterise local climate variability. Use of the CWB circumvents uncertainties in the soil water balance stemming from limited data on subsurface properties. We also focus on three key input variables used to calculate the CWB: precipitation, mean temperature and diurnal temperature range. We use local observations to evaluate the skill of gridded datasets –specifically meteorological reanalyses – in representing local conditions. Reanalysis estimates of Mukteshwar climate showed large absolute biases but accurately captured the timing and relative amplitude of the annual cycle of these three variables and the CWB. This suggests that the reanalyses can provide insight regarding climate processes in data-sparse regions, but caution is necessary if extracting absolute values. While the local observations at Mukteshwar show clear annual cycles and substantial interannual variability, results from investigation of their time-dependency were quite mixed. Pragmatically this implies that while “change is coming, variability is now.” If communities can adapt to the observed historical hydroclimate variability they will have built meaningful adaptive capacity to cope with on-going environmental change. This follows a ‘low regret’ approach advocated when facing a substantially uncertain future.

Hourly precipitation extremes can intensify with temperature at higher rates than expected from thermodynamic increases explained by the Clausius-Clapeyron (CC) relationship (~6.5%/K), but local scaling with surface air temperature is highly variable. Here we use daily dewpoint temperature, a direct proxy of absolute humidity, to estimate at-gauge local scaling across six macro-regions for a global dataset of over 7000 hourly precipitation gauges. We find scaling rates from CC to 2xCC at more than 60% of gauges, peaking in the tropics at a median rate of ~1.5CC. Moreover, regional scaling rates show surprisingly universal behaviour at around CC, with higher scaling in Europe. Importantly for impacts, hourly scaling is persistently higher than scaling for daily extreme precipitation. Our results indicate greater consistency in global scaling than previous work, usually at or above CC, with positive scaling in the (sub)tropics. This demonstrates the relevance of dewpoint temperature scaling to understanding future changes.