Intensity-duration-frequency (IDF) curves require accurate observations which are not available everywhere. To provide globally consistent IDF maps, we harness the accuracy of Global Sub-Daily Rainfall (GSDR) gauge observations and combine this with the power of a random forest regression model to regionalise the parameters of a the SMEV (Simplified Metastatistical Extreme Value) distribution. After regionalisation, it is possible to compute intensities for any combination of return period and durations up to 24 hours. These regionalised intensities are named BURGER, the ”Bottom Up Regionalised Global Extreme Rainfall dataset”. Comparing intensities from BURGER against those obtained at GSDR stations shows that errors increase with less frequent events. Median percentage biases range between -20 % and 35 %, with a median around 0 %, yet with marked regional variations. Despite results indicating a too light tail, their agreement with expected intensities is still good. Intensities from simulations excluding station data in the UK and Germany deviate up to 15 % from those obtained with the station data included. A benchmark with the remote sensing-based GPEX dataset did not reveal structurally lower agreement in ungauged regions compared to gauged regions, suggesting the transfer to ungauged areas works reasonably well. Comparing results with NOAA data shows that different data and methodologies can hamper a robust benchmark: while at some GSDR stations NOAA data agrees with BURGER data, it hardly agrees with empirically derived intensities at other stations. This first bottom-up approach to global IDF data yields promising results and insights warranting future improvements.

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.

Although observations and modelling studies show that heavy rainfall is increasing in many regions, how changes will manifest themselves on sub-daily timescales remains highly uncertain. Here, for the first time, we combine observational analysis and high-resolution modelling results to examine changes to extreme rainfall intensities in urbanized Kuala Lumpur, Malaysia. We find that hourly intensities of extreme rainfall have increased by ~35% over the last three decades, nearly three times more than in surrounding rural areas, with daily intensities showing much weaker increases. Our modelling results confirm that the urban heat island effect creates a more unstable atmosphere, increased vertical uplift and moisture convergence. This, combined with weak surface winds in the Tropics, causes intensification of rainfall extremes over the city, with reduced rainfall in the surrounding region.

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.

River systems originating from the Upper Indus Basin (UIB) are dominated by runoff from snow and glacier melt and summer monsoonal rainfall. These water resources are highly stressed as huge populations of people living in this region depend on them, including for agriculture, domestic use, and energy production. Projections suggest that the UIB region will be affected by considerable (yet poorly quantified) changes to the seasonality and composition of runoff in the future, which are likely to have considerable impacts on these supplies. Given how directly and indirectly communities and ecosystems are dependent on these resources and the growing pressure on them due to ever-increasing demands, the impacts of climate change pose considerable adaptation challenges. The strong linkages between hydroclimate, cryosphere, water resources, and human activities within the UIB suggest that a multi- and inter-disciplinary research approach integrating the social and natural/environmental sciences is critical for successful adaptation to ongoing and future hydrological and climate change. Here we use a horizon scanning technique to identify the Top 100 questions related to the most pressing knowledge gaps and research priorities in social and natural sciences on climate change and water in the UIB. These questions are on the margins of current thinking and investigation and are clustered into 14 themes, covering three overarching topics of ‘governance, policy, and sustainable solutions’, ‘socioeconomic processes and livelihoods’, and ‘integrated Earth System processes’. Raising awareness of these cutting-edge knowledge gaps and opportunities will hopefully encourage researchers, funding bodies, practitioners, and policy makers to address them.