Using a two-year dataset (2016–17) from 17 one-minute rain gauges located in the moist forest region of Ghana, the performance of Integrated Multisatellite Retrievals for GPM, version 6b (IMERG), is evaluated based on a subdaily time scale, down to the level of the underlying passive microwave (PMW) and infrared (IR) sources. Additionally, the spaceborne cloud product Cloud Property Dataset Using SEVIRI, edition 2 (CLAAS-2), available every 15 min, is used to link IMERG rainfall to cloud-top properties. Several important issues are identified: 1) IMERG’s proneness to low-intensity false alarms, accounting for more than a fifth of total rainfall; 2) IMERG’s overestimation of the rainfall amount from frequently occurring weak convective events, while that of relatively rare but strong mesoscale convective systems is underestimated, resulting in an error compensation; and 3) a decrease of skill during the little dry season in July and August, known to feature enhanced low-level cloudiness and warm rain. These findings are related to 1) a general oversensitivity for clouds with low ice and liquid water path and a particular oversensitivity for low cloud optical thickness, a problem which is slightly reduced for direct PMW overpasses; 2) a pronounced negative bias for high rain intensities, strongest when IR data are included; and 3) a large fraction of missed events linked with rainfall out of warm clouds, which are inherently misinterpreted by IMERG and its sources. This paper emphasizes the potential of validating spaceborne rainfall products with high-resolution rain gauges on a subdaily time scale, particularly for the understudied West African region.

Rain‐fed agriculture is predominantly the primary source of livelihood in sub‐Saharan Africa. In Ghana, it employs over 60% of the population and contributes to about 30% of the Gross Domestic Product (GDP). Despite the relevance of rain‐fed agriculture to the socioe‐conomic development of the country, studies investigating the impacts of climate variability on crop yield in the region are limited. This prevents the growth of relevant policy frameworks to address the threats posed by climate variability on crop production. As a contribution to guide policy planning and food security in Ghana, the present study seeks to analyze the vulnerability of maize yield to rainfall, soil moisture, and minimum and maximum temperature variability in Ghana. Moreover, the study assesses the impact of below‐normal and above‐normal rainfall conditions on maize yield in the country. The results show a decreasing trend in rainfall and soil moisture and an increasing trend in the maize yield and minimum and maximum temperature in Ghana. The individual climate variables (rainfall, soil moisture, minimum and maximum temperature) account for approximately 4.2, 22.5, 39.2, and 23.1%, respectively, of the annual maize yield variations throughout the study period. Multivariate regression analysis revealed that soil moisture and minimum and maximum temperature account for approximately 75% of the variation of maize yield under wetter‐than‐normal conditions in Ghana. This implies that, under wetter‐than‐normal rainfall conditions, 25% of the variation in maize yield could be attributable to non‐climatic conditions or practices. In like manner, climatic parameters account for 40.8% of maize yield under drier‐than‐normal rainfall conditions in the country. This study, therefore, recommends investment in climate‐smart agronomic technologies such as access to improved seed, extension and mechanization services, and other farm management practices to maximize maize yield in the country.

In regions of sparse gauge networks, satellite rainfall products are mostly used as surrogate measurements for various rainfall impact studies. Their potential to complement rain gauge measurements is influenced by the uncertainties associated with them. This study evaluates the performance of satellites and merged rainfall products over Ghana in order to provide information on the consistency and reliability of such products. Satellite products were validated with gridded rain gauge data from the Ghana Meteorological Agency (GMet) on various time scales. It was observed that the performance of the products in the country are mostly scale and location dependent. In addition, most of the products showed relatively good skills on the seasonal scale (r > 0.90) rather than the annual, and, after removal of seasonality from the datasets, except ARC2 that had larger biases in most cases. Again, all products captured the onsets, cessations, and spells countrywide and in the four agro-ecological zones. However, CHIRPS particularly revealed a better skill on both seasonal and annual scales countrywide. The products were not affected by the number of gauge stations within a grid cell in the Forest and Transition zones. This study, therefore, recommends all products except ARC2 for climate impact studies over the region.

Rainfall onset and cessation date greatly influence cropping calendar decisions in rain-fed agricultural systems. This paper examined trends of onsets, cessation, and the length of growing season over Northern Ghana using CHIRPS-v2, gauge, and farmers’ perceptions data between 1981 and 2019. Results from CHIRPS-v2 revealed that the three seasonal rainfall indices have substantial latitudinal variability. Significant late and early onsets were observed at the West and East of 1.5° W longitude, respectively. Significant late cessations and longer growing periods occurred across Northern Ghana. The ability of farmers’ perceptions and CHIRPS-v2 to capture rainfall onsets are time and location-dependent. A total of 71% of farmers rely on traditional knowledge to forecast rainfall onsets. Adaptation measures applied were not always consistent with the rainfall seasonality. More investment in modern climate information services is required to complement the existing local knowledge of forecasting rainfall seasonality.