We investigated the nonlinearity of runoff response to global mean temperature (GMT) change in the Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models at the river basin scale globally. Results show that changes in long‐term mean annual runoff are nonlinear with GMT rise over most extended subtropical basins, suggesting that estimation of future runoff change derived from the linear scaling relations would be biased. As for the interannual variability, nonlinearities are apparent mainly in central and western Asia, southern and western Africa, most of Europe, and Australia when GMT increases beyond 1.5°C. This suggests that impacts of climate change under 1.5°C GMT rise on runoff variability should not be simply scaled from that under a 2°C warming world. Our results highlight the contrasting response of areal runoff to GMT rise across global major river basins and reveal the threshold of GMT increment at which the nonlinear runoff response is projected to emerge.

Existing gauging networks are sparse and not readily available in real-time over the transboundary Lancang–Mekong River (LMR) basin, making it difficult to accurately identify drought. In this study, we aimed to build an operational real-time Lancang–Mekong drought monitor (LMDM), through combining satellite real-time data and the Variable Infiltration Capacity (VIC) hydrological model at a 0.25° spatial resolution. Toward this, three VIC runs were conducted: (1) a 60-year (1951–2010) historical simulation driven by Princeton’s global meteorological forcing (PGF) for yielding ‘normal’ conditions (PGF-VIC), wherein the VIC was calibrated with 20-year observed streamflow at six hydrological stations; (2) a short-period (2011–2014) simulation to bridge the gap between the historical and the real-time modeling; (3) the real-time (2015–present) simulation driven by bias-corrected satellite data, wherein the real-time soil moisture (SM) estimate was expressed as percentile (relative to the ‘normal’) for drought monitoring. Results show that VIC can successfully reproduce the observed hydrographs, with the Nash–Sutcliffe efficiency exceeding 0.70 and the relative bias mostly within 15%. Assessment on the performance of LMDM shows that the real-time SM estimates bear good spatial similarity to the reference, with the correlation coefficient beyond 0.80 across >70% of the domain. In terms of drought monitoring, the LMDM can reasonably reproduce the two recorded droughts, implying extreme droughts covering the Lower LMR during 2004/05 and widespread severe 2009/10 drought across the upper domain. The percentage drought area implied by the LMDM and the reference is close, corresponding to 66% and 60%, 43% and 40%, and 44% and 36% for each typical drought month. Since January 2015, the LMDM was running in an operational mode, from which the 2016 unprecedented drought was successfully identified in Mekong Delta. This study highlights the LMDM’s capability for reliable real-time drought monitoring, which can serve as a valuable drought early warning prototype for other data-poor regions.

Knowledge of the interplays between soil moisture (SM) and precipitation (P) across different temporal scales is crucial to understand the land‐atmosphere interactions. To examine the factors influencing SM–P coherences, observed long‐term SM and P datasets were obtained from two regional networks (i.e., the Nebraska Mesonet and part of the Soil Climate Analysis Network in Utah) within the continental United States. We evaluated the intricate SM–P interactions by checking high‐ and low‐frequency components using wavelet analysis. The analysis revealed that the global coherence coefficients (GCCs) between SM and P at all frequencies tended to be greater under wetter climatic conditions, suggesting that higher P could promote the SM–P interactions. Moreover, GCCs were more correlated with meteorological factors in Nebraska with wetter climates (e.g., positively with mean annual P and negatively with aridity index at high‐frequency periodicities (daily to 3 monthly), and opposite interactions at low‐frequency periodicities (3 monthly to yearly); whereas, significant correlations emerged between GCCs and soil texture at all frequency periodicities in Utah (e.g., positively with sand fraction while negatively with silt and clay fractions). Part of the reason for the impact of soil texture in Utah was that as the P periodicities weakened with increasing climatic dryness in Utah, soil texture played a more important role in controlling infiltration processes, which directly affected SM–P coherences. Overall, this study demonstrates that SM–P interactions at regional scales were possibly affected by the relative importance of meteorological and local land surface conditions in controlling SM dynamics.