Mountain regions worldwide present a pronounced spatiotemporal precipitation variability, which added to scarce monitoring networks limits our understanding of the generation processes involved. To improve our understanding of clouds and precipitation dynamics and cross-scale generation processes in mountain regions, we analyzed spatiotemporal rainfall patterns using satellite cloud products (SCP) in the Paute basin (900–4200 m a.s.l. and 6481 km²) in the Andes of Ecuador. Precipitation models, using SCP and GIS data, reveal the spatial extension of three regimes: a three-modal (TM) regime present across the basin, a bimodal (BM) regime, along sheltered valleys, and a unimodal (UM) regime at windward slopes of the eastern cordillera. Subsequently, the spatiotemporal analysis using synoptic information shows that the dry season of the BM regime during boreal summer is caused by strong subsidence inhibiting convective clouds formation. Meanwhile, in UM regions, low advective shallow cap clouds mainly cause precipitation, influenced by water vapor from the Amazon and enhanced easterlies during boreal summer. TM regions are transition zones from UM to BM and zones on the windward slopes of the western cordillera. These results highlight the suitability of satellite and GIS data-driven statistical models to study spatiotemporal rainfall seasonality and generation processes in complex terrain, as the Andes.

Downscaling improves considerably the results of General Circulation Models (GCMs). However, little information is available on the performance of downscaling methods in the Andean mountain region. The paper presents the downscaling of monthly precipitation estimates of the NCEP/NCAR reanalysis 1 applying the statistical downscaling model (SDSM), artificial neural networks (ANNs), and the least squares support vector machines (LS-SVM) approach. Downscaled monthly precipitation estimates after bias and variance correction were compared to the median and variance of the 30-year observations of 5 climate stations in the Paute River basin in southern Ecuador, one of Ecuador’s main river basins. A preliminary comparison revealed that both artificial intelligence methods, ANN and LS-SVM, performed equally. Results disclosed that ANN and LS-SVM methods depict, in general, better skills in comparison to SDSM. However, in some months, SDSM estimates matched the median and variance of the observed monthly precipitation depths better. Since synoptic variables do not always present local conditions, particularly in the period going from September to December, it is recommended for future studies to refine estimates of downscaling, for example, by combining dynamic and statistical methods, or to select sets of synoptic predictors for specific months or seasons.

Spatial prediction of precipitation with high resolution is a challenging task in regions with strong climate variability and scarce monitoring. For this purpose, the quasi-continuous supply of information from satellite imagery is commonly used to complement in situ data. However, satellite images of precipitation are available at coarse resolutions, and require adequate methods for spatial downscaling and calibration. The objective of this paper is to introduce and evaluate a 2-step spatial downscaling approach for monthly precipitation applied to TRMM 3B43 (from $0.{25}^{\circ }\approx$ 27 km to 5 km resolution), resulting in 5 downscaled products for the period 01-2001/12-2011. The methodology was evaluated in 3 contrasting climatic regions of Ecuador. In step 1, bilinear resampling was applied over TRMM, and used as a reference product. The second step introduces further variability, and consists of four alternative gauge-satellite merging methods: (1) regression with in situ stations, (2) regression kriging with in situ stations, (3) regression with in situ stations and auxiliary variables, and (4) regression kriging with in situ stations and auxiliary variables. The first 2 methods only use the resampled TRMM data set as an independent variable. The last 2 methods enrich these models with auxiliary environmental factors, incorporating atmospheric and land variables. The results showed that no product outperforms the others in every region. In general, the methods with residual kriging correction outperformed the regression models. Regression kriging with situ data provided the best representation in the Coast, while regression kriging with in situ and auxiliary data generated the best results in the Andes. In the Amazon, no product outperformed the resampled TRMM images, probably due to the low density of in situ stations. These results are relevant to enhance satellite precipitation, depending on the availability of in situ data, auxiliary satellite variables and the particularities of the climatic regions.

Mesoscale convective systems (MCSs) climatology, the thermodynamic and dynamical variables, and teleconnections influencing MCSs development are assessed for the Paute basin (PB) in the Ecuadorian Andes from 2000 to 2009. The seasonality of MCSs occurrence shows a bimodal pattern, with higher occurrence during March-April (MA) and October-November (ON), analogous to the regional rainfall seasonality. The diurnal cycle of MCSs shows a clear nocturnal occurrence, especially during the MA and ON periods. Interestingly, despite the higher occurrence of MCSs during the rainy seasons, the monthly size relative frequency remains fairly constant throughout the year. On the east of the PB, the persistent high convective available potential and low convective inhibition values from midday to nighttime are likely related to the nocturnal development of the MCSs. A significant positive correlation between the MCSs occurrence to the west of the PB and the Trans-Niño index was found, suggesting that ENSO is an important source of interannual variability of MCSs frequency with increasing development of MCSs during warm ENSO phases. On the east of the PB, the variability of MCSs is positively correlated to the tropical Atlantic sea surface temperature anomalies south of the equator, due to the variability of the Atlantic subtropical anticyclone, showing main departures from this relation when anomalous conditions occur in the tropical Pacific due to ENSO.

The identification of area‐wise homogeneous precipitation regions helps to unveil similar precipitation patterns and amounts, where similar atmospheric processes at diverse temporal scales are likely to occur. However, although scientifically and socially relevant, the regionalization of precipitation is challenging, specially in areas of complex orography and with sparse monitoring. This limits our understanding of complex spatio‐temporal dependencies and hinders any information‐based resource management decision‐making. Gridded satellite precipitation products are useful in this context, even though they contain bias errors. Spatial functional data analysis (sFDA) is a novel technique that considers time as well as space dependencies by means of spatial autocorrelation and complete time functions, one for each spatial point. Therefore, the aim of this study is to evaluate sFDA as a tool to regionalize seasonality and intensity precipitation patterns, having Ecuador as a case study. The Tropical Rainfall Measuring Mission (TRMM 3B43) satellite precipitation is used to create an exhaustive spatial delineation. To the best of our knowledge, this is the first time that a sFDA regionalization approach is performed on gridded satellite precipitation. The complex orography and heat‐driven atmospheric processes in Ecuador's latitude make it a highly non‐trivial case to test the aforementioned technique. As a result, five relevant regions of precipitation seasonality were spatially delineated and temporally characterized. Three of them were zonally oriented, and two meridional‐wise in the coast. In addition, 20 relevant intensity regions across Ecuador were identified specially in regions with sparse monitoring. The regions were related to regional climate processes. However, limitations were found in regions with important orographic precipitation and locally variability patterns, probably due to the shortcomings of TRMM precipitation quantification. After the successful application of hierarchical regionalization using sFDA in a tropical region with sparse monitoring, it is reasonable to conclude that sFDA is a robust method to detect compact and meaningful homogeneous areas.

Droughts are one of the most spatially extensive disasters that are faced by societies. Despite the urgency to define mitigation strategies, little research has been done regarding droughts related to climate change. The challenges are due to the complexity of droughts and to future precipitation uncertainty from Global Climate Models (GCMs). It is well-known that climate change will have more impact on developing countries. This is the case for Ecuador, which also has the additional challenges of lacking meteorological drought studies covering its three main regions: Coast, Highlands, and Amazon, and of having an intricate orography. Thus, this study assesses the spatio-temporal characteristics of present and future droughts in Ecuador under Representative Concentrations Pathways (RCP) 4.5 and 8.5. The 10 km dynamically downscaled products (DGCMs) from Coupled Model Intercomparison Project 5 (CMIP5) was used. The Standardized Precipitation Index (SPI) for droughts was calculated pixel-wise for present time 1981–2005 and for future time 2041-2070. The results showed a slightly decreasing trend for future droughts for the whole country, with a larger reduction for moderate droughts, followed by severe and extreme drought events. In the Coast and Highland regions, the intra-annual analysis showed a reduction of moderate and severe future droughts for RCP 4.5 and for RCP 8.5 throughout the year. Extreme droughts showed small and statistically non-significant decreases. In the Amazon region, moderate droughts showed increases from May to October, and decreases for the rest of the year. Additionally, severe drought increases are expected from May to December, and decreases from January to April. Finally, extreme drought increases are expected from January to April, with larger increases in October and November. Thus, in the Amazon, the rainy period showed a decreasing trend of droughts, following the wetter in wet- and drier in dry paradigm. Climate change causes decision-making process and calls for adaptation strategies being more challenging. In this context, our study has contributed to better mapping the space-time evolution of future drought risk in Ecuador, thus providing valuable information for water management and decision making as Ecuador faces climate change.