A non-homogeneous hidden Markov model (NHMM) is developed using a 40-year record (1950–1990) of daily rainfall at 11 stations in Tanzania and National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) re-analysis atmospheric fields of a number of meteorological variables. The following atmospheric fields, temperature at 1000 hPa, geo-potential height at 1000 hPa, meridional winds and zonal winds at 850 hPa, and zonal winds at the equator from 10 to 1000 hPa, in a region defined by 25°S–25°N and 25°–75°E are identified as appropriate predictors for the downscaling of the seasonal regime of daily rainfall in Tanzania. The NHMM is used to predict future rainfall patterns under a comparatively high greenhouse gas emissions scenario [Representative Concentration Pathway 8.5 (RCP8.5)], using predictors from the CMCC-CMS (Centro Mediterraneo sui Cambiamenti Climatici) simulations from 1950 to 2100. Instead of pre-specifying a fixed rainy season, the model considers seasonality of precipitation to be determined by the 21st century simulations of the atmospheric variables used as predictors. The future downscaled precipitation simulations for the RCP8.5 scenario indicate that in the 21st century Tanzania may experience: (1) a slight decrease in the number of wet days and seasonal rainfall in MAM and JJAS, but not in OND; (2) a reduction of annual total rainfall; and (3) an intensification of the frequency and intensity of extreme rainfall, as identified by 90th, 95th, and 99th percentiles.

River floods cause extensive losses to economy, ecology, and society throughout the world. They are driven by the space‐time structure of catchment rainfall, which is determined by large‐scale, or even global‐scale, atmospheric processes. The identification of coherent, large‐scale atmospheric circulation structures that determine the moisture transport and convergence associated with rainfall‐induced flooding can help improve its predictability and phenomenology. In this paper, we extend a methodology, used for the analysis of extreme rainfall events, to high streamflow events (HSEs). The approach combines multiple machine learning methods to link HSEs to atmospheric circulation patterns. An application to the German streamflow network using reanalysis data for the period 1960 to 2012 is presented. Daily streamflow from 166 gauges, homogeneously distributed across Germany, are used. Geopotential height fields and integrated vapor transport (IVT) are derived from reanalysis data. An unsupervised neural network, Self Organizing Maps, is applied to geopotential height to identify a finite number of circulation patterns (CPs). Event synchronization between CPs and HSEs is used to establish if they are linked or not. If they are linked, the Event Synchronization method computes the delay between the occurrence of a CP and a HSE. Finally, local logistic regression is used to estimate the probability of occurrence of a HSE, as function of CP and IVT. We demonstrate that our approach is very effective to evaluate HSE probability occurrence across Germany.

Global circulation models (GCMs) are routinely used to project future climate conditions worldwide, such as temperature and precipitation. However, inputs with a finer resolution are required to drive impact‐related models at local scales. The non‐homogeneous hidden Markov model (NHMM) is a widely used algorithm for the precipitation statistical downscaling for GCMs. To improve the accuracy of the traditional NHMM in reproducing spatiotemporal precipitation features of specific geographic sites, especially extreme precipitation, we developed a new precipitation downscaling framework. This hierarchical model includes two levels: (1) establishing an ensemble learning model to predict the occurrence probabilities for different levels of daily precipitation aggregated at multiple sites and (2) constructing a NHMM downscaling scheme of daily amount at the scale of a single rain gauge using the outputs of ensemble learning model as predictors. As the results obtained for the case study in the central‐eastern China (CEC), show that our downscaling model is highly efficient and performs better than the NHMM in simulating precipitation variability and extreme precipitation. Finally, our projections indicate that CEC may experience increased precipitation in the future. Compared with ~26 years (1990–2015), the extreme precipitation frequency and amount would significantly increase by 21.9%–48.1% and 12.3%–38.3%, respectively, by the late century (2075–2100) under the Shared Socioeconomic Pathway 585 climate scenario.