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There are few commonly used indicators that describe the state of Earth’s global hydrological cycle and here we propose three indicators to capture how an increased greenhouse effect influences the global hydrological cycle and the associated rainfall patterns. They are: i) the 24-hr global total rainfall, ii) the global surface area with daily precipitation, and iii) the global mean precipitation intensity. With a recent progress in both global satellite observations and reanalyses, we can now estimate the global rainfall surface area to provide new insights into how rainfall intensity changes over time. Based on the ERA5 reanalysis, we find that the global area of daily precipitation decreased from 43 to 41% of the global area between 1950 and 2020, whereas the total daily global rainfall increased from 1440 Gt to 1510 Gt per day. However, the estimated 24-hr global precipitation surface area varies when estimated from different reanalyses and the estimates are still uncertain. To further investigate historical variations in the precipitation surface area, we carried out a wavelet analysis of 24-hr precipitation from the ERA5 reanalysis that indicated how the rainfall patterns have changed over time. Our results suggest that individual precipitation systems over the globe have shrunk in terms of their spatial extent while becoming more intense throughout the period 1950–2020. Hence, the wavelet results are in line with an acceleration of the rate of the global hydrological cycle, combined with a diminishing global area of rainfall.

Precipitation plays an important role in the Arctic hydrological cycle, affecting different areas like the surface energy budget and the mass balance of glaciers. Thus, accurate measurements of precipitation are crucial for physical process studies, but gauge measurements in the Arctic are sparse and subject to relocations and several gauge issues. From Svalbard, we analyze precipitation trends at six weather stations for the last 50–100 years by combining different observation series and adjusting for inhomogeneities. For the past 50 years, the measured annual precipitation has increased by 30%–45%. However, precipitation measurements in the cold and windy climate are strongly influenced by gauge undercatch. Correcting for undercatch reduces the trend values by 10% points, since the fraction of solid precipitation has decreased and undercatch is larger for solid precipitation. Thus, precipitation corrected for undercatch should be used to study “true” precipitation trends in the Arctic. Precipitation over Svalbard has been modeled by downscaling reanalysis data to a spatial resolution of 1 km. In general, the modeled annual precipitation is higher (13%–175%) than the measured values and mainly higher than the precipitation corrected for undercatch. Although the model resolves orographic effects on a regional scale, the downscaling is not able to reproduce local orographic enhancement for onshore winds, nor local effects of rain shadow. The downscaled dataset explains approximately 60% of the interannual precipitation variability. The model-based trends during 1979–2018 are positive, but weaker (~4% decade−1) than the observed (~8% decade−1) trends.

A simple formula for estimating approximate values of return levels for sub-daily rainfall is presented and tested. It was derived from a combination of simple mathematical principles, approximations and fitted to 10 year return levels taken from intensity-duration-frequency (IDF) curves representing 14 sites in Oslo. The formula was subsequently evaluated against IDF curves from independent sites elsewhere in Norway. Since it only needs 24 h rain gauge data as input, it can provide approximate estimates for the IDF curves used to describe sub-daily rainfall return levels. In this respect, it can be considered as means of downscaling with respect to timescale, given an approximate power-law dependency between temporal scales. One clear benefit with this framework is that observational data is far more abundant for 24 h rain gauge records than for sub-daily measurements. Furthermore, it does not assume stationarity, and is well-suited for projecting IDF curves for a future climate.

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