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Recent studies disagree on how rainfall extremes over India have changed in space and time over the past half century 1-4 , as well as on whether the changes observed are due to global warming 5,6 or regional urbanization 7 . Although a uniform and consistent decrease in moderate rainfall has been reported 1,3 , a lack of agreement about trends in heavy rainfall may be due in part to differences in the characterization and spatial averaging of extremes. Here we use extreme value theory [8][9][10][11][12][13][14][15] to examine trends in Indian rainfall over the past half century in the context of long-term, low-frequency variability. We show that when generalized extreme value theory 8,16-18 is applied to annual maximum rainfall over India, no statistically significant spatially uniform trends are observed, in agreement with previous studies using different approaches 2-4 . Furthermore, our space-time regression analysis of the return levels points to increasing spatial variability of rainfall extremes over India. Our findings highlight the need for systematic examination of global versus regional drivers of trends in Indian rainfall extremes, and may help to inform flood hazard preparedness and water resource management in the region.There is considerable debate in the recent literature about the nature of space-time trends in extreme rainfall over India 1-3 and their attribution to aspects of global change, specifically, global climate change 5,6 versus regional urbanization patterns 7 . Previous researchers have drawn a variety of conclusions 1-4 about trends in rainfall extremes during the Indian monsoon from a regular 1 • ×1 • (or similar) gridded daily rainfall dataset over India for 1951-2003. The differences can probably be attributed to the corresponding definitions of extremes, levels of spatial aggregation and areas of coverage. The use of fixed thresholds over a 12 • × 10 • box labelled as Central India suggested an increasing trend in rainfall extremes concurrent with decreasing moderate rainfall, resulting in no discernible net trends 1 . However, the use of variable percentile-based thresholds over each individual 1 • × 1 • grid 2 , analysis based on homogeneous regions 3 and analysis with a percentile-based definition of the frequency and intensity of rainfall extremes 4 showed no discernible spatially uniform trends in rainfall extremes over India. Field significance tests do not statistically support the hypothesis of increasing trends in heavy rain events 2,3 , and the region labelled as Central India in ref. 1 may not be meteorologically homogeneous 19 . However, the use of additional data (1901-2004; ref. 5) confirmed the findings of ref. 1 when identical definitions of extremes, aggregations and coverage were used. The often conflicting insights about Indian rainfall extremes in the recent literature point to the importance of effective characterization of extremes, especially for understanding and communicating their relevance to impacts and policy.Here we show that rainfall extremes over I...

Access to daily high-resolution gridded surface weather data based on direct observations and over long time periods is essential for many studies and applications including vegetation, wildlife, soil health, hydrological modelling, and as driver data in Earth system models. We present Daymet V4, a 40-year daily meteorological dataset on a 1 km grid for North America, Hawaii, and Puerto Rico, providing temperature, precipitation, shortwave radiation, vapor pressure, snow water equivalent, and day length. The dataset includes an objective quantification of uncertainty based on strict cross-validation analysis for temperature and precipitation results. The dataset represents several improvements from a previous version, and this data descriptor provides complete documentation for updated methods. Improvements include: reductions in the timing bias of input reporting weather station measurements; improvement to the three-dimensional regression model techniques in the core algorithm; and a novel approach to handling high elevation temperature measurement biases. We show cross-validation analyses with the underlying weather station data to demonstrate the technical validity of new dataset generation methods, and to quantify improved accuracy.

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[1] Recent hydrologic studies on multivariate stochastic analysis have indicated that copulas perform well for bivariate problems. In particular, the Frank family of Archimedean copulas has been a popular choice for a dependence model. However, there are limitations to extending such Archimedean copulas to trivariate and higher dimensions, with very specific restrictions on the kinds of dependencies that can be modeled. In this study, we examine a non-Archimedean copula from the Plackett family that is founded on the theory of constant cross-product ratio. It is shown that the Plackett family not only performs well at the bivariate level, but also allows a trivariate stochastic analysis where the lower-level dependencies between variables can be fully preserved while allowing for specificity at the trivariate level as well. The feasible range of Plackett parameters that would result in valid 3-copulas is determined numerically. The trivariate Plackett family of copulas is then applied to the study of temporal distribution of extreme rainfall events for several stations in Indiana where the estimated parameters lie in the feasible region. On the basis of a given rainfall depth and duration, conditional expectations of rainfall features such as expected peak intensity, time to peak, and percentage cumulative rainfall at 10% cumulative time increments are evaluated. The results of this study suggest that while the constant cross-product ratio theory was conventionally applied to discrete type random variables, it is also applicable to continuous random variables, and that it provides further flexibility for multivariate stochastic analyses of rainfall.